A Probabilistic and Highly Efficient Topology Control Algorithm for Underwater Cooperating AUV Networks

PubMed Central

Li, Ning; Cürüklü, Baran; Bastos, Joaquim; Sucasas, Victor; Fernandez, Jose Antonio Sanchez; Rodriguez, Jonathan

2017-01-01

The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV’s parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power adjustment ratio while improving the network performance. PMID:28471387

A Probabilistic and Highly Efficient Topology Control Algorithm for Underwater Cooperating AUV Networks.

PubMed

Li, Ning; Cürüklü, Baran; Bastos, Joaquim; Sucasas, Victor; Fernandez, Jose Antonio Sanchez; Rodriguez, Jonathan

2017-05-04

The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV's parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power adjustment ratio while improving the network performance.

Implementation of Maximum Power Point Tracking (MPPT) Solar Charge Controller using Arduino

NASA Astrophysics Data System (ADS)

Abdelilah, B.; Mouna, A.; KouiderM’Sirdi, N.; El Hossain, A.

2018-05-01

the platform Arduino with a number of sensors standard can be used as components of an electronic system for acquiring measures and controls. This paper presents the design of a low-cost and effective solar charge controller. This system includes several elements such as the solar panel converter DC/DC, battery, circuit MPPT using Microcontroller, sensors, and the MPPT algorithm. The MPPT (Maximum Power Point Tracker) algorithm has been implemented using an Arduino Nano with the preferred program. The voltage and current of the Panel are taken where the program implemented will work and using this algorithm that MPP will be reached. This paper provides details on the solar charge control device at the maximum power point. The results include the change of the duty cycle with the change in load and thus mean the variation of the buck converter output voltage and current controlled by the MPPT algorithm.

Advanced power system protection and incipient fault detection and protection of spaceborne power systems

NASA Technical Reports Server (NTRS)

Russell, B. Don

1989-01-01

This research concentrated on the application of advanced signal processing, expert system, and digital technologies for the detection and control of low grade, incipient faults on spaceborne power systems. The researchers have considerable experience in the application of advanced digital technologies and the protection of terrestrial power systems. This experience was used in the current contracts to develop new approaches for protecting the electrical distribution system in spaceborne applications. The project was divided into three distinct areas: (1) investigate the applicability of fault detection algorithms developed for terrestrial power systems to the detection of faults in spaceborne systems; (2) investigate the digital hardware and architectures required to monitor and control spaceborne power systems with full capability to implement new detection and diagnostic algorithms; and (3) develop a real-time expert operating system for implementing diagnostic and protection algorithms. Significant progress has been made in each of the above areas. Several terrestrial fault detection algorithms were modified to better adapt to spaceborne power system environments. Several digital architectures were developed and evaluated in light of the fault detection algorithms.

Maximum wind energy extraction strategies using power electronic converters

NASA Astrophysics Data System (ADS)

Wang, Quincy Qing

2003-10-01

This thesis focuses on maximum wind energy extraction strategies for achieving the highest energy output of variable speed wind turbine power generation systems. Power electronic converters and controls provide the basic platform to accomplish the research of this thesis in both hardware and software aspects. In order to send wind energy to a utility grid, a variable speed wind turbine requires a power electronic converter to convert a variable voltage variable frequency source into a fixed voltage fixed frequency supply. Generic single-phase and three-phase converter topologies, converter control methods for wind power generation, as well as the developed direct drive generator, are introduced in the thesis for establishing variable-speed wind energy conversion systems. Variable speed wind power generation system modeling and simulation are essential methods both for understanding the system behavior and for developing advanced system control strategies. Wind generation system components, including wind turbine, 1-phase IGBT inverter, 3-phase IGBT inverter, synchronous generator, and rectifier, are modeled in this thesis using MATLAB/SIMULINK. The simulation results have been verified by a commercial simulation software package, PSIM, and confirmed by field test results. Since the dynamic time constants for these individual models are much different, a creative approach has also been developed in this thesis to combine these models for entire wind power generation system simulation. An advanced maximum wind energy extraction strategy relies not only on proper system hardware design, but also on sophisticated software control algorithms. Based on literature review and computer simulation on wind turbine control algorithms, an intelligent maximum wind energy extraction control algorithm is proposed in this thesis. This algorithm has a unique on-line adaptation and optimization capability, which is able to achieve maximum wind energy conversion efficiency through continuously improving the performance of wind power generation systems. This algorithm is independent of wind power generation system characteristics, and does not need wind speed and turbine speed measurements. Therefore, it can be easily implemented into various wind energy generation systems with different turbine inertia and diverse system hardware environments. In addition to the detailed description of the proposed algorithm, computer simulation results are presented in the thesis to demonstrate the advantage of this algorithm. As a final confirmation of the algorithm feasibility, the algorithm has been implemented inside a single-phase IGBT inverter, and tested with a wind simulator system in research laboratory. Test results were found consistent with the simulation results. (Abstract shortened by UMI.)

Intelligent power management in a vehicular system with multiple power sources

NASA Astrophysics Data System (ADS)

Murphey, Yi L.; Chen, ZhiHang; Kiliaris, Leonidas; Masrur, M. Abul

This paper presents an optimal online power management strategy applied to a vehicular power system that contains multiple power sources and deals with largely fluctuated load requests. The optimal online power management strategy is developed using machine learning and fuzzy logic. A machine learning algorithm has been developed to learn the knowledge about minimizing power loss in a Multiple Power Sources and Loads (M_PS&LD) system. The algorithm exploits the fact that different power sources used to deliver a load request have different power losses under different vehicle states. The machine learning algorithm is developed to train an intelligent power controller, an online fuzzy power controller, FPC_MPS, that has the capability of finding combinations of power sources that minimize power losses while satisfying a given set of system and component constraints during a drive cycle. The FPC_MPS was implemented in two simulated systems, a power system of four power sources, and a vehicle system of three power sources. Experimental results show that the proposed machine learning approach combined with fuzzy control is a promising technology for intelligent vehicle power management in a M_PS&LD power system.

Sliding-mode control of single input multiple output DC-DC converter

NASA Astrophysics Data System (ADS)

Zhang, Libo; Sun, Yihan; Luo, Tiejian; Wan, Qiyang

2016-10-01

Various voltage levels are required in the vehicle mounted power system. A conventional solution is to utilize an independent multiple output DC-DC converter whose cost is high and control scheme is complicated. In this paper, we design a novel SIMO DC-DC converter with sliding mode controller. The proposed converter can boost the voltage of a low-voltage input power source to a controllable high-voltage DC bus and middle-voltage output terminals, which endow the converter with characteristics of simple structure, low cost, and convenient control. In addition, the sliding mode control (SMC) technique applied in our converter can enhance the performances of a certain SIMO DC-DC converter topology. The high-voltage DC bus can be regarded as the main power source to the high-voltage facility of the vehicle mounted power system, and the middle-voltage output terminals can supply power to the low-voltage equipment on an automobile. In the respect of control algorithm, it is the first time to propose the SMC-PID (Proportion Integration Differentiation) control algorithm, in which the SMC algorithm is utilized and the PID control is attended to the conventional SMC algorithm. The PID control increases the dynamic ability of the SMC algorithm by establishing the corresponding SMC surface and introducing the attached integral of voltage error, which endow the sliding-control system with excellent dynamic performance. At last, we established the MATLAB/SIMULINK simulation model, tested performance of the system, and built the hardware prototype based on Digital Signal Processor (DSP). Results show that the sliding mode control is able to track a required trajectory, which has robustness against the uncertainties and disturbances.

Sliding-mode control of single input multiple output DC-DC converter.

PubMed

Zhang, Libo; Sun, Yihan; Luo, Tiejian; Wan, Qiyang

2016-10-01

Various voltage levels are required in the vehicle mounted power system. A conventional solution is to utilize an independent multiple output DC-DC converter whose cost is high and control scheme is complicated. In this paper, we design a novel SIMO DC-DC converter with sliding mode controller. The proposed converter can boost the voltage of a low-voltage input power source to a controllable high-voltage DC bus and middle-voltage output terminals, which endow the converter with characteristics of simple structure, low cost, and convenient control. In addition, the sliding mode control (SMC) technique applied in our converter can enhance the performances of a certain SIMO DC-DC converter topology. The high-voltage DC bus can be regarded as the main power source to the high-voltage facility of the vehicle mounted power system, and the middle-voltage output terminals can supply power to the low-voltage equipment on an automobile. In the respect of control algorithm, it is the first time to propose the SMC-PID (Proportion Integration Differentiation) control algorithm, in which the SMC algorithm is utilized and the PID control is attended to the conventional SMC algorithm. The PID control increases the dynamic ability of the SMC algorithm by establishing the corresponding SMC surface and introducing the attached integral of voltage error, which endow the sliding-control system with excellent dynamic performance. At last, we established the MATLAB/SIMULINK simulation model, tested performance of the system, and built the hardware prototype based on Digital Signal Processor (DSP). Results show that the sliding mode control is able to track a required trajectory, which has robustness against the uncertainties and disturbances.

Power optimization of digital baseband WCDMA receiver components on algorithmic and architectural level

NASA Astrophysics Data System (ADS)

Schämann, M.; Bücker, M.; Hessel, S.; Langmann, U.

2008-05-01

High data rates combined with high mobility represent a challenge for the design of cellular devices. Advanced algorithms are required which result in higher complexity, more chip area and increased power consumption. However, this contrasts to the limited power supply of mobile devices. This presentation discusses the application of an HSDPA receiver which has been optimized regarding power consumption with the focus on the algorithmic and architectural level. On algorithmic level the Rake combiner, Prefilter-Rake equalizer and MMSE equalizer are compared regarding their BER performance. Both equalizer approaches provide a significant increase of performance for high data rates compared to the Rake combiner which is commonly used for lower data rates. For both equalizer approaches several adaptive algorithms are available which differ in complexity and convergence properties. To identify the algorithm which achieves the required performance with the lowest power consumption the algorithms have been investigated using SystemC models regarding their performance and arithmetic complexity. Additionally, for the Prefilter Rake equalizer the power estimations of a modified Griffith (LMS) and a Levinson (RLS) algorithm have been compared with the tool ORINOCO supplied by ChipVision. The accuracy of this tool has been verified with a scalable architecture of the UMTS channel estimation described both in SystemC and VHDL targeting a 130 nm CMOS standard cell library. An architecture combining all three approaches combined with an adaptive control unit is presented. The control unit monitors the current condition of the propagation channel and adjusts parameters for the receiver like filter size and oversampling ratio to minimize the power consumption while maintaining the required performance. The optimization strategies result in a reduction of the number of arithmetic operations up to 70% for single components which leads to an estimated power reduction of up to 40% while the BER performance is not affected. This work utilizes SystemC and ORINOCO for the first estimation of power consumption in an early step of the design flow. Thereby algorithms can be compared in different operating modes including the effects of control units. Here an algorithm having higher peak complexity and power consumption but providing more flexibility showed less consumption for normal operating modes compared to the algorithm which is optimized for peak performance.

Engineering platform and experimental protocol for design and evaluation of a neurally-controlled powered transfemoral prosthesis.

PubMed

Zhang, Fan; Liu, Ming; Harper, Stephen; Lee, Michael; Huang, He

2014-07-22

To enable intuitive operation of powered artificial legs, an interface between user and prosthesis that can recognize the user's movement intent is desired. A novel neural-machine interface (NMI) based on neuromuscular-mechanical fusion developed in our previous study has demonstrated a great potential to accurately identify the intended movement of transfemoral amputees. However, this interface has not yet been integrated with a powered prosthetic leg for true neural control. This study aimed to report (1) a flexible platform to implement and optimize neural control of powered lower limb prosthesis and (2) an experimental setup and protocol to evaluate neural prosthesis control on patients with lower limb amputations. First a platform based on a PC and a visual programming environment were developed to implement the prosthesis control algorithms, including NMI training algorithm, NMI online testing algorithm, and intrinsic control algorithm. To demonstrate the function of this platform, in this study the NMI based on neuromuscular-mechanical fusion was hierarchically integrated with intrinsic control of a prototypical transfemoral prosthesis. One patient with a unilateral transfemoral amputation was recruited to evaluate our implemented neural controller when performing activities, such as standing, level-ground walking, ramp ascent, and ramp descent continuously in the laboratory. A novel experimental setup and protocol were developed in order to test the new prosthesis control safely and efficiently. The presented proof-of-concept platform and experimental setup and protocol could aid the future development and application of neurally-controlled powered artificial legs.

Grid Integration of Single Stage Solar PV System using Three-level Voltage Source Converter

NASA Astrophysics Data System (ADS)

Hussain, Ikhlaq; Kandpal, Maulik; Singh, Bhim

2016-08-01

This paper presents a single stage solar PV (photovoltaic) grid integrated power generating system using a three level voltage source converter (VSC) operating at low switching frequency of 900 Hz with robust synchronizing phase locked loop (RS-PLL) based control algorithm. To track the maximum power from solar PV array, an incremental conductance algorithm is used and this maximum power is fed to the grid via three-level VSC. The use of single stage system with three level VSC offers the advantage of low switching losses and the operation at high voltages and high power which results in enhancement of power quality in the proposed system. Simulated results validate the design and control algorithm under steady state and dynamic conditions.

Power Converter Control Algorithm Design and Simulation for the NREL Next-Generation Drivetrain: July 8, 2013 - January 7, 2016

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blodgett, Douglas; Behnke, Michael; Erdman, William

The National Renewable Energy Laboratory (NREL) and NREL Next-Generation Drivetrain Partners are developing a next-generation drivetrain (NGD) design as part of a Funding Opportunity Announcement award from the U.S. Department of Energy. The proposed NGD includes comprehensive innovations to the gearbox, generator, and power converter that increase the gearbox reliability and drivetrain capacity, while lowering deployment and operation and maintenance costs. A key task within this development effort is the power converter fault control algorithm design and associated computer simulations using an integrated electromechanical model of the drivetrain. The results of this task will be used in generating the embeddedmore » control software to be utilized in the power converter during testing of the NGD in the National Wind Technology Center 2.5-MW dynamometer. A list of issues to be addressed with these algorithms was developed by review of the grid interconnection requirements of various North American transmission system operators, and those requirements that presented the greatest impact to the wind turbine drivetrain design were then selected for mitigation via power converter control algorithms.« less

A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.

PubMed

Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan

2015-01-01

In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.

A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs

PubMed Central

Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan

2015-01-01

In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network. PMID:26571042

The Type-2 Fuzzy Logic Controller-Based Maximum Power Point Tracking Algorithm and the Quadratic Boost Converter for Pv System

NASA Astrophysics Data System (ADS)

Altin, Necmi

2018-05-01

An interval type-2 fuzzy logic controller-based maximum power point tracking algorithm and direct current-direct current (DC-DC) converter topology are proposed for photovoltaic (PV) systems. The proposed maximum power point tracking algorithm is designed based on an interval type-2 fuzzy logic controller that has an ability to handle uncertainties. The change in PV power and the change in PV voltage are determined as inputs of the proposed controller, while the change in duty cycle is determined as the output of the controller. Seven interval type-2 fuzzy sets are determined and used as membership functions for input and output variables. The quadratic boost converter provides high voltage step-up ability without any reduction in performance and stability of the system. The performance of the proposed system is validated through MATLAB/Simulink simulations. It is seen that the proposed system provides high maximum power point tracking speed and accuracy even for fast changing atmospheric conditions and high voltage step-up requirements.

Voltage stability index based optimal placement of static VAR compensator and sizing using Cuckoo search algorithm

NASA Astrophysics Data System (ADS)

Venkateswara Rao, B.; Kumar, G. V. Nagesh; Chowdary, D. Deepak; Bharathi, M. Aruna; Patra, Stutee

2017-07-01

This paper furnish the new Metaheuristic algorithm called Cuckoo Search Algorithm (CSA) for solving optimal power flow (OPF) problem with minimization of real power generation cost. The CSA is found to be the most efficient algorithm for solving single objective optimal power flow problems. The CSA performance is tested on IEEE 57 bus test system with real power generation cost minimization as objective function. Static VAR Compensator (SVC) is one of the best shunt connected device in the Flexible Alternating Current Transmission System (FACTS) family. It has capable of controlling the voltage magnitudes of buses by injecting the reactive power to system. In this paper SVC is integrated in CSA based Optimal Power Flow to optimize the real power generation cost. SVC is used to improve the voltage profile of the system. CSA gives better results as compared to genetic algorithm (GA) in both without and with SVC conditions.

Integrated controls design optimization

DOEpatents

Lou, Xinsheng; Neuschaefer, Carl H.

2015-09-01

A control system (207) for optimizing a chemical looping process of a power plant includes an optimizer (420), an income algorithm (230) and a cost algorithm (225) and a chemical looping process models. The process models are used to predict the process outputs from process input variables. Some of the process in puts and output variables are related to the income of the plant; and some others are related to the cost of the plant operations. The income algorithm (230) provides an income input to the optimizer (420) based on a plurality of input parameters (215) of the power plant. The cost algorithm (225) provides a cost input to the optimizer (420) based on a plurality of output parameters (220) of the power plant. The optimizer (420) determines an optimized operating parameter solution based on at least one of the income input and the cost input, and supplies the optimized operating parameter solution to the power plant.

A Combined Energy Management Algorithm for Wind Turbine/Battery Hybrid System

NASA Astrophysics Data System (ADS)

Altin, Necmi; Eyimaya, Süleyman Emre

2018-03-01

From an energy management standpoint, natural phenomena such as solar irradiation and wind speed are uncontrolled variables, so the correlation between the energy generated by renewable energy sources and energy demand cannot always be predicted. For this reason, energy storage systems are used to provide more efficient renewable energy systems. In these systems, energy management systems are used to control the energy storage system and establish a balance between the generated power and the power demand. In addition, especially in wind turbines, rapidly varying wind speeds cause wind power fluctuations, which threaten the power system stability, especially at high power levels. Energy storage systems are also used to mitigate the power fluctuations and sustain the power system's stability. In these systems, another controller which controls the energy storage system power to mitigate power fluctuations is required. These two controllers are different from each other. In this study, a combined energy management algorithm is proposed which can perform both as an energy control system and a power fluctuation mitigation system. The proposed controller is tested with wind energy conversion system modeled in MATLAB/Simulink. Simulation results show that the proposed controller acts as an energy management system while, at the same time, mitigating power fluctuations.

Evaluation of Dynamic Channel and Power Assignment for Cognitive Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Syed A. Ahmad; Umesh Shukla; Ryan E. Irwin

2011-03-01

In this paper, we develop a unifying optimization formulation to describe the Dynamic Channel and Power Assignment (DCPA) problem and evaluation method for comparing DCPA algorithms. DCPA refers to the allocation of transmit power and frequency channels to links in a cognitive network so as to maximize the total number of feasible links while minimizing the aggregate transmit power. We apply our evaluation method to five algorithms representative of DCPA used in literature. This comparison illustrates the tradeoffs between control modes (centralized versus distributed) and channel/power assignment techniques. We estimate the complexity of each algorithm. Through simulations, we evaluate themore » effectiveness of the algorithms in achieving feasible link allocations in the network, as well as their power efficiency. Our results indicate that, when few channels are available, the effectiveness of all algorithms is comparable and thus the one with smallest complexity should be selected. The Least Interfering Channel and Iterative Power Assignment (LICIPA) algorithm does not require cross-link gain information, has the overall lowest run time, and highest feasibility ratio of all the distributed algorithms; however, this comes at a cost of higher average power per link.« less

Performance optimization of a photovoltaic chain conversion by the PWM control

NASA Astrophysics Data System (ADS)

Rezoug, M. R.; Chenni, R.

2017-02-01

The interest of the research technique of maximum power point tracking, exposed by this article, lays in the fact of work instantly on the real characteristic of the photovoltaic module. This work is based on instantaneous measurements of its terminals' current & voltage as well as the exploitation of the characteristic "Power - Duty Cycle" to define rapidly the Duty cycle in which power reaches its maximum value. To ensure instantaneous tracking of the point of maximum power, we use "DC/DC Converter" based on "Pulse Wave Modulation's (PWM) Command" controlled by an algorithm implanted in a microcontroller's memory. This algorithm responds to the quick changes in climate (sunlight and temperature). To identify the control parameters "VPV & IPV" at any change in operating conditions, sensors are projected. this algorithm applied to the Duty cycle of the static converter enables the control of power supplied by the photovoltaic generator thanks to oscillatory movement around the MPP. Our article highlights the importance of this technique which lays in its simplicity and performance in changing climatic conditions. This efficiency is confirmed by experimental tests and this technique will improve its predecessors.

Electromagnetic interference-aware transmission scheduling and power control for dynamic wireless access in hospital environments.

PubMed

Phunchongharn, Phond; Hossain, Ekram; Camorlinga, Sergio

2011-11-01

We study the multiple access problem for e-Health applications (referred to as secondary users) coexisting with medical devices (referred to as primary or protected users) in a hospital environment. In particular, we focus on transmission scheduling and power control of secondary users in multiple spatial reuse time-division multiple access (STDMA) networks. The objective is to maximize the spectrum utilization of secondary users and minimize their power consumption subject to the electromagnetic interference (EMI) constraints for active and passive medical devices and minimum throughput guarantee for secondary users. The multiple access problem is formulated as a dual objective optimization problem which is shown to be NP-complete. We propose a joint scheduling and power control algorithm based on a greedy approach to solve the problem with much lower computational complexity. To this end, an enhanced greedy algorithm is proposed to improve the performance of the greedy algorithm by finding the optimal sequence of secondary users for scheduling. Using extensive simulations, the tradeoff in performance in terms of spectrum utilization, energy consumption, and computational complexity is evaluated for both the algorithms.

Automatic control algorithm effects on energy production

NASA Technical Reports Server (NTRS)

Mcnerney, G. M.

1981-01-01

A computer model was developed using actual wind time series and turbine performance data to simulate the power produced by the Sandia 17-m VAWT operating in automatic control. The model was used to investigate the influence of starting algorithms on annual energy production. The results indicate that, depending on turbine and local wind characteristics, a bad choice of a control algorithm can significantly reduce overall energy production. The model can be used to select control algorithms and threshold parameters that maximize long term energy production. The results from local site and turbine characteristics were generalized to obtain general guidelines for control algorithm design.

The Design of Power System Stability Controller Based on the PCH Theory and Improved Genetic Algorithm

NASA Astrophysics Data System (ADS)

Liu, Zhijian; Yin, Donghui; Yan, Jun

2017-05-01

Low frequency oscillation is still frequently happened in the power system and it affects the safety and stability of power system directly. With the continuously expending of the interconnection scale of power grid, the risk of low frequency oscillation becomes more and more noticeable. Firstly, the basic theory of port-controlled Hamilton (PCH) and its application is analyzed. Secondly, based on the PCH theory and the dynamic model of system, from the viewpoint of energy, the nonlinear stability controller of power system is designed. By the improved genetic algorithm, the parameters of the PCH model are optimized. Finally, a simulation model with PCH is built to vary the effectiveness of the method proposed in this paper.

A Nonlinear Digital Control Solution for a DC/DC Power Converter

NASA Technical Reports Server (NTRS)

Zhu, Minshao

2002-01-01

A digital Nonlinear Proportional-Integral-Derivative (NPID) control algorithm was proposed to control a 1-kW, PWM, DC/DC, switching power converter. The NPID methodology is introduced and a practical hardware control solution is obtained. The design of the controller was completed using Matlab (trademark) Simulink, while the hardware-in-the-loop testing was performed using both the dSPACE (trademark) rapid prototyping system, and a stand-alone Texas Instruments (trademark) Digital Signal Processor (DSP)-based system. The final Nonlinear digital control algorithm was implemented and tested using the ED408043-1 Westinghouse DC-DC switching power converter. The NPID test results are discussed and compared to the results of a standard Proportional-Integral (PI) controller.

Mathematical analysis and coordinated current allocation control in battery power module systems

NASA Astrophysics Data System (ADS)

Han, Weiji; Zhang, Liang

2017-12-01

As the major energy storage device and power supply source in numerous energy applications, such as solar panels, wind plants, and electric vehicles, battery systems often face the issue of charge imbalance among battery cells/modules, which can accelerate battery degradation, cause more energy loss, and even incur fire hazard. To tackle this issue, various circuit designs have been developed to enable charge equalization among battery cells/modules. Recently, the battery power module (BPM) design has emerged to be one of the promising solutions for its capability of independent control of individual battery cells/modules. In this paper, we propose a new current allocation method based on charging/discharging space (CDS) for performance control in BPM systems. Based on the proposed method, the properties of CDS-based current allocation with constant parameters are analyzed. Then, real-time external total power requirement is taken into account and an algorithm is developed for coordinated system performance control. By choosing appropriate control parameters, the desired system performance can be achieved by coordinating the module charge balance and total power efficiency. Besides, the proposed algorithm has complete analytical solutions, and thus is very computationally efficient. Finally, the efficacy of the proposed algorithm is demonstrated using simulations.

Novel Straight and Circular Road Driving Control of Electric Power Assisted Wheelchair Based on Fuzzy Algorithm

NASA Astrophysics Data System (ADS)

Seki, Hirokazu; Tadakuma, Susumu

This paper describes a novel straight and circular road driving control scheme for electric power assisted wheelchairs. “Electric power assisted wheelchair” which assists the driving force by electric motors is expected to be widely used as a mobility support system for elderly people and disabled people, however, the performance of the straight and circular road driving must be further improved because the two wheels drive independently. This paper proposes a novel driving control scheme based on fuzzy algorithm to realize the stable and reliable driving on straight and circular roads. The suitable assisted torque of the right and left wheels is determined by fuzzy algorithm based on the posture angular velocity of the wheelchair and the human input torque proportion of the right and left wheels. Some experiments on the practical roads show the effectiveness of the proposed control system.

Operationality Improvement Control of Electric Power Assisted Wheelchair by Fuzzy Algorithm Considering Posture Angle

NASA Astrophysics Data System (ADS)

Murakami, Hiroki; Seki, Hirokazu; Minakata, Hideaki; Tadakuma, Susumu

This paper describes a novel operationality improvement control for electric power assisted wheelchairs. “Electric power assisted wheelchair” which assists the driving force by electric motors is expected to be widely used as a mobility support system for elderly people and disabled people, however, the performance of the straight and circular road driving must be further improved because the two wheels drive independently. This paper proposes a novel operationality improvement control by fuzzy algorithm to realize the stable driving on straight and circular roads. The suitable assisted torque of the right and left wheels is determined by fuzzy algorithm based on the posture angular velocity, the posture angle of the wheelchair, the human input torque proportion and the total human torque of the right and left wheels. Some experiments on the practical roads show the effectiveness of the proposed control system.

Network, system, and status software enhancements for the autonomously managed electrical power system breadboard. Volume 2: Protocol specification

NASA Technical Reports Server (NTRS)

Mckee, James W.

1990-01-01

This volume (2 of 4) contains the specification, structured flow charts, and code listing for the protocol. The purpose of an autonomous power system on a spacecraft is to relieve humans from having to continuously monitor and control the generation, storage, and distribution of power in the craft. This implies that algorithms will have been developed to monitor and control the power system. The power system will contain computers on which the algorithms run. There should be one control computer system that makes the high level decisions and sends commands to and receive data from the other distributed computers. This will require a communications network and an efficient protocol by which the computers will communicate. One of the major requirements on the protocol is that it be real time because of the need to control the power elements.

Power efficient control algorithm of electromechanical unbalance vibration exciter with induction motor

NASA Astrophysics Data System (ADS)

Topovskiy, V. V.; Simakov, G. M.

2017-10-01

A control algorithm of an electromechanical unbalance vibration exciter that provides a free rotational movement is offered in the paper. The unbalance vibration exciter control system realizing a free rotational movement has been synthesized. The structured modeling of the synthesized system has been carried out and its transients are presented. The advantages and disadvantages of the proposed control algorithm applied to the unbalance vibration exciter are shown.

SDR input power estimation algorithms

NASA Astrophysics Data System (ADS)

Briones, J. C.; Nappier, J. M.

The General Dynamics (GD) S-Band software defined radio (SDR) in the Space Communications and Navigation (SCAN) Testbed on the International Space Station (ISS) provides experimenters an opportunity to develop and demonstrate experimental waveforms in space. The SDR has an analog and a digital automatic gain control (AGC) and the response of the AGCs to changes in SDR input power and temperature was characterized prior to the launch and installation of the SCAN Testbed on the ISS. The AGCs were used to estimate the SDR input power and SNR of the received signal and the characterization results showed a nonlinear response to SDR input power and temperature. In order to estimate the SDR input from the AGCs, three algorithms were developed and implemented on the ground software of the SCAN Testbed. The algorithms include a linear straight line estimator, which used the digital AGC and the temperature to estimate the SDR input power over a narrower section of the SDR input power range. There is a linear adaptive filter algorithm that uses both AGCs and the temperature to estimate the SDR input power over a wide input power range. Finally, an algorithm that uses neural networks was designed to estimate the input power over a wide range. This paper describes the algorithms in detail and their associated performance in estimating the SDR input power.

SDR Input Power Estimation Algorithms

NASA Technical Reports Server (NTRS)

Nappier, Jennifer M.; Briones, Janette C.

2013-01-01

The General Dynamics (GD) S-Band software defined radio (SDR) in the Space Communications and Navigation (SCAN) Testbed on the International Space Station (ISS) provides experimenters an opportunity to develop and demonstrate experimental waveforms in space. The SDR has an analog and a digital automatic gain control (AGC) and the response of the AGCs to changes in SDR input power and temperature was characterized prior to the launch and installation of the SCAN Testbed on the ISS. The AGCs were used to estimate the SDR input power and SNR of the received signal and the characterization results showed a nonlinear response to SDR input power and temperature. In order to estimate the SDR input from the AGCs, three algorithms were developed and implemented on the ground software of the SCAN Testbed. The algorithms include a linear straight line estimator, which used the digital AGC and the temperature to estimate the SDR input power over a narrower section of the SDR input power range. There is a linear adaptive filter algorithm that uses both AGCs and the temperature to estimate the SDR input power over a wide input power range. Finally, an algorithm that uses neural networks was designed to estimate the input power over a wide range. This paper describes the algorithms in detail and their associated performance in estimating the SDR input power.

Nonlinear Recurrent Neural Network Predictive Control for Energy Distribution of a Fuel Cell Powered Robot

PubMed Central

Chen, Qihong; Long, Rong; Quan, Shuhai

2014-01-01

This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell. PMID:24707206

Hardware Implementation of Maximum Power Point Tracking for Thermoelectric Generators

NASA Astrophysics Data System (ADS)

Maganga, Othman; Phillip, Navneesh; Burnham, Keith J.; Montecucco, Andrea; Siviter, Jonathan; Knox, Andrew; Simpson, Kevin

2014-06-01

This work describes the practical implementation of two maximum power point tracking (MPPT) algorithms, namely those of perturb and observe, and extremum seeking control. The proprietary dSPACE system is used to perform hardware in the loop (HIL) simulation whereby the two control algorithms are implemented using the MATLAB/Simulink (Mathworks, Natick, MA) software environment in order to control a synchronous buck-boost converter connected to two commercial thermoelectric modules. The process of performing HIL simulation using dSPACE is discussed, and a comparison between experimental and simulated results is highlighted. The experimental results demonstrate the validity of the two MPPT algorithms, and in conclusion the benefits and limitations of real-time implementation of MPPT controllers using dSPACE are discussed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qin, SB; Cady, ST; Dominguez-Garcia, AD

This paper presents the theory and implementation of a distributed algorithm for controlling differential power processing converters in photovoltaic (PV) applications. This distributed algorithm achieves true maximum power point tracking of series-connected PV submodules by relying only on local voltage measurements and neighbor-to-neighbor communication between the differential power converters. Compared to previous solutions, the proposed algorithm achieves reduced number of perturbations at each step and potentially faster tracking without adding extra hardware; all these features make this algorithm well-suited for long submodule strings. The formulation of the algorithm, discussion of its properties, as well as three case studies are presented.more » The performance of the distributed tracking algorithm has been verified via experiments, which yielded quantifiable improvements over other techniques that have been implemented in practice. Both simulations and hardware experiments have confirmed the effectiveness of the proposed distributed algorithm.« less

Investigation on application of genetic algorithms to optimal reactive power dispatch of power systems

NASA Astrophysics Data System (ADS)

Wu, Q. H.; Ma, J. T.

1993-09-01

A primary investigation into application of genetic algorithms in optimal reactive power dispatch and voltage control is presented. The application was achieved, based on (the United Kingdom) National Grid 48 bus network model, using a novel genetic search approach. Simulation results, compared with that obtained using nonlinear programming methods, are included to show the potential of applications of the genetic search methodology in power system economical and secure operations.

Distributed Optimal Power Flow of AC/DC Interconnected Power Grid Using Synchronous ADMM

NASA Astrophysics Data System (ADS)

Liang, Zijun; Lin, Shunjiang; Liu, Mingbo

2017-05-01

Distributed optimal power flow (OPF) is of great importance and challenge to AC/DC interconnected power grid with different dispatching centres, considering the security and privacy of information transmission. In this paper, a fully distributed algorithm for OPF problem of AC/DC interconnected power grid called synchronous ADMM is proposed, and it requires no form of central controller. The algorithm is based on the fundamental alternating direction multiplier method (ADMM), by using the average value of boundary variables of adjacent regions obtained from current iteration as the reference values of both regions for next iteration, which realizes the parallel computation among different regions. The algorithm is tested with the IEEE 11-bus AC/DC interconnected power grid, and by comparing the results with centralized algorithm, we find it nearly no differences, and its correctness and effectiveness can be validated.

Analysis and simulation tools for solar array power systems

NASA Astrophysics Data System (ADS)

Pongratananukul, Nattorn

This dissertation presents simulation tools developed specifically for the design of solar array power systems. Contributions are made in several aspects of the system design phases, including solar source modeling, system simulation, and controller verification. A tool to automate the study of solar array configurations using general purpose circuit simulators has been developed based on the modeling of individual solar cells. Hierarchical structure of solar cell elements, including semiconductor properties, allows simulation of electrical properties as well as the evaluation of the impact of environmental conditions. A second developed tool provides a co-simulation platform with the capability to verify the performance of an actual digital controller implemented in programmable hardware such as a DSP processor, while the entire solar array including the DC-DC power converter is modeled in software algorithms running on a computer. This "virtual plant" allows developing and debugging code for the digital controller, and also to improve the control algorithm. One important task in solar arrays is to track the maximum power point on the array in order to maximize the power that can be delivered. Digital controllers implemented with programmable processors are particularly attractive for this task because sophisticated tracking algorithms can be implemented and revised when needed to optimize their performance. The proposed co-simulation tools are thus very valuable in developing and optimizing the control algorithm, before the system is built. Examples that demonstrate the effectiveness of the proposed methodologies are presented. The proposed simulation tools are also valuable in the design of multi-channel arrays. In the specific system that we have designed and tested, the control algorithm is implemented on a single digital signal processor. In each of the channels the maximum power point is tracked individually. In the prototype we built, off-the-shelf commercial DC-DC converters were utilized. At the end, the overall performance of the entire system was evaluated using solar array simulators capable of simulating various I-V characteristics, and also by using an electronic load. Experimental results are presented.

Power system monitoring and source control of the Space Station Freedom DC power system testbed

NASA Technical Reports Server (NTRS)

Kimnach, Greg L.; Baez, Anastacio N.

1992-01-01

Unlike a terrestrial electric utility which can purchase power from a neighboring utility, the Space Station Freedom (SSF) has strictly limited energy resources; as a result, source control, system monitoring, system protection, and load management are essential to the safe and efficient operation of the SSF Electric Power System (EPS). These functions are being evaluated in the DC Power Management and Distribution (PMAD) Testbed which NASA LeRC has developed at the Power System Facility (PSF) located in Cleveland, Ohio. The testbed is an ideal platform to develop, integrate, and verify power system monitoring and control algorithms. State Estimation (SE) is a monitoring tool used extensively in terrestrial electric utilities to ensure safe power system operation. It uses redundant system information to calculate the actual state of the EPS, to isolate faulty sensors, to determine source operating points, to verify faults detected by subsidiary controllers, and to identify high impedance faults. Source control and monitoring safeguard the power generation and storage subsystems and ensure that the power system operates within safe limits while satisfying user demands with minimal interruptions. System monitoring functions, in coordination with hardware implemented schemes, provide for a complete fault protection system. The objective of this paper is to overview the development and integration of the state estimator and the source control algorithms.

Power system monitoring and source control of the Space Station Freedom dc-power system testbed

NASA Technical Reports Server (NTRS)

Kimnach, Greg L.; Baez, Anastacio N.

1992-01-01

Unlike a terrestrial electric utility which can purchase power from a neighboring utility, the Space Station Freedom (SSF) has strictly limited energy resources; as a result, source control, system monitoring, system protection, and load management are essential to the safe and efficient operation of the SSF Electric Power System (EPS). These functions are being evaluated in the dc Power Management and Distribution (PMAD) Testbed which NASA LeRC has developed at the Power System Facility (PSF) located in Cleveland, Ohio. The testbed is an ideal platform to develop, integrate, and verify power system monitoring and control algorithms. State Estimation (SE) is a monitoring tool used extensively in terrestrial electric utilities to ensure safe power system operation. It uses redundant system information to calculate the actual state of the EPS, to isolate faulty sensors, to determine source operating points, to verify faults detected by subsidiary controllers, and to identify high impedance faults. Source control and monitoring safeguard the power generation and storage subsystems and ensure that the power system operates within safe limits while satisfying user demands with minimal interruptions. System monitoring functions, in coordination with hardware implemented schemes, provide for a complete fault protection system. The objective of this paper is to overview the development and integration of the state estimator and the source control algorithms.

Design and analysis of tilt integral derivative controller with filter for load frequency control of multi-area interconnected power systems.

PubMed

Kumar Sahu, Rabindra; Panda, Sidhartha; Biswal, Ashutosh; Chandra Sekhar, G T

2016-03-01

In this paper, a novel Tilt Integral Derivative controller with Filter (TIDF) is proposed for Load Frequency Control (LFC) of multi-area power systems. Initially, a two-area power system is considered and the parameters of the TIDF controller are optimized using Differential Evolution (DE) algorithm employing an Integral of Time multiplied Absolute Error (ITAE) criterion. The superiority of the proposed approach is demonstrated by comparing the results with some recently published heuristic approaches such as Firefly Algorithm (FA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) optimized PID controllers for the same interconnected power system. Investigations reveal that proposed TIDF controllers provide better dynamic response compared to PID controller in terms of minimum undershoots and settling times of frequency as well as tie-line power deviations following a disturbance. The proposed approach is also extended to two widely used three area test systems considering nonlinearities such as Generation Rate Constraint (GRC) and Governor Dead Band (GDB). To improve the performance of the system, a Thyristor Controlled Series Compensator (TCSC) is also considered and the performance of TIDF controller in presence of TCSC is investigated. It is observed that system performance improves with the inclusion of TCSC. Finally, sensitivity analysis is carried out to test the robustness of the proposed controller by varying the system parameters, operating condition and load pattern. It is observed that the proposed controllers are robust and perform satisfactorily with variations in operating condition, system parameters and load pattern. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Neural Network based Control of SG based Standalone Generating System with Energy Storage for Power Quality Enhancement

NASA Astrophysics Data System (ADS)

Nayar, Priya; Singh, Bhim; Mishra, Sukumar

2017-08-01

An artificial intelligence based control algorithm is used in solving power quality problems of a diesel engine driven synchronous generator with automatic voltage regulator and governor based standalone system. A voltage source converter integrated with a battery energy storage system is employed to mitigate the power quality problems. An adaptive neural network based signed regressor control algorithm is used for the estimation of the fundamental component of load currents for control of a standalone system with load leveling as an integral feature. The developed model of the system performs accurately under varying load conditions and provides good dynamic response to the step changes in loads. The real time performance is achieved using MATLAB along with simulink/simpower system toolboxes and results adhere to an IEEE-519 standard for power quality enhancement.

Microgrids | Grid Modernization | NREL

Science.gov Websites

algorithms for microgrid integration Controller hardware-in-the-loop testing, where the physical controller interacts with a model of the microgrid and associated power devices Power hardware-in-the-loop testing of operation was validated in a power hardware-in-the-loop experiment using a programmable DC power supply to

A novel symbiotic organisms search algorithm for congestion management in deregulated environment

NASA Astrophysics Data System (ADS)

Verma, Sumit; Saha, Subhodip; Mukherjee, V.

2017-01-01

In today's competitive electricity market, managing transmission congestion in deregulated power system has created challenges for independent system operators to operate the transmission lines reliably within the limits. This paper proposes a new meta-heuristic algorithm, called as symbiotic organisms search (SOS) algorithm, for congestion management (CM) problem in pool based electricity market by real power rescheduling of generators. Inspired by interactions among organisms in ecosystem, SOS algorithm is a recent population based algorithm which does not require any algorithm specific control parameters unlike other algorithms. Various security constraints such as load bus voltage and line loading are taken into account while dealing with the CM problem. In this paper, the proposed SOS algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results, thus, obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the proposed SOS algorithm for obtaining the higher quality solution is also established.

A novel symbiotic organisms search algorithm for congestion management in deregulated environment

NASA Astrophysics Data System (ADS)

Verma, Sumit; Saha, Subhodip; Mukherjee, V.

2017-01-01

In today's competitive electricity market, managing transmission congestion in deregulated power system has created challenges for independent system operators to operate the transmission lines reliably within the limits. This paper proposes a new meta-heuristic algorithm, called as symbiotic organisms search (SOS) algorithm, for congestion management (CM) problem in pool-based electricity market by real power rescheduling of generators. Inspired by interactions among organisms in ecosystem, SOS algorithm is a recent population-based algorithm which does not require any algorithm specific control parameters unlike other algorithms. Various security constraints such as load bus voltage and line loading are taken into account while dealing with the CM problem. In this paper, the proposed SOS algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results, thus, obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the proposed SOS algorithm for obtaining the higher quality solution is also established.

Soft-Fault Detection Technologies Developed for Electrical Power Systems

NASA Technical Reports Server (NTRS)

Button, Robert M.

2004-01-01

The NASA Glenn Research Center, partner universities, and defense contractors are working to develop intelligent power management and distribution (PMAD) technologies for future spacecraft and launch vehicles. The goals are to provide higher performance (efficiency, transient response, and stability), higher fault tolerance, and higher reliability through the application of digital control and communication technologies. It is also expected that these technologies will eventually reduce the design, development, manufacturing, and integration costs for large, electrical power systems for space vehicles. The main focus of this research has been to incorporate digital control, communications, and intelligent algorithms into power electronic devices such as direct-current to direct-current (dc-dc) converters and protective switchgear. These technologies, in turn, will enable revolutionary changes in the way electrical power systems are designed, developed, configured, and integrated in aerospace vehicles and satellites. Initial successes in integrating modern, digital controllers have proven that transient response performance can be improved using advanced nonlinear control algorithms. One technology being developed includes the detection of "soft faults," those not typically covered by current systems in use today. Soft faults include arcing faults, corona discharge faults, and undetected leakage currents. Using digital control and advanced signal analysis algorithms, we have shown that it is possible to reliably detect arcing faults in high-voltage dc power distribution systems (see the preceding photograph). Another research effort has shown that low-level leakage faults and cable degradation can be detected by analyzing power system parameters over time. This additional fault detection capability will result in higher reliability for long-lived power systems such as reusable launch vehicles and space exploration missions.

A Novel Control Strategy for Autonomous Operation of Isolated Microgrid with Prioritized Loads

NASA Astrophysics Data System (ADS)

Kumar, R. Hari; Ushakumari, S.

2018-05-01

Maintenance of power balance between generation and demand is one of the most critical requirements for the stable operation of a power system network. To mitigate the power imbalance during the occurrence of any disturbance in the system, fast acting algorithms are inevitable. This paper proposes a novel algorithm for load shedding and network reconfiguration in an isolated microgrid with prioritized loads and multiple islands, which will help to quickly restore the system in the event of a fault. The performance of the proposed algorithm is enhanced using genetic algorithm and its effectiveness is illustrated with simulation results on modified Consortium for Electric Reliability Technology Solutions (CERTS) microgrid.

Microgrid Enabled Distributed Energy Solutions (MEDES) Fort Bliss Military Reservation

DTIC Science & Technology

2014-02-01

Logic Controller PF Power Factor PO Performance Objectives PPA Power Purchase Agreements PV Photovoltaic R&D Research and Development RDSI...controller, algorithms perform power flow analysis, short term optimization, and long-term forecasted planning. The power flow analysis ensures...renewable photovoltaic power and energy storage in this microgrid configuration, the available mission operational time of the backup generator can be

Multi-agent coordination algorithms for control of distributed energy resources in smart grids

NASA Astrophysics Data System (ADS)

Cortes, Andres

Sustainable energy is a top-priority for researchers these days, since electricity and transportation are pillars of modern society. Integration of clean energy technologies such as wind, solar, and plug-in electric vehicles (PEVs), is a major engineering challenge in operation and management of power systems. This is due to the uncertain nature of renewable energy technologies and the large amount of extra load that PEVs would add to the power grid. Given the networked structure of a power system, multi-agent control and optimization strategies are natural approaches to address the various problems of interest for the safe and reliable operation of the power grid. The distributed computation in multi-agent algorithms addresses three problems at the same time: i) it allows for the handling of problems with millions of variables that a single processor cannot compute, ii) it allows certain independence and privacy to electricity customers by not requiring any usage information, and iii) it is robust to localized failures in the communication network, being able to solve problems by simply neglecting the failing section of the system. We propose various algorithms to coordinate storage, generation, and demand resources in a power grid using multi-agent computation and decentralized decision making. First, we introduce a hierarchical vehicle-one-grid (V1G) algorithm for coordination of PEVs under usage constraints, where energy only flows from the grid in to the batteries of PEVs. We then present a hierarchical vehicle-to-grid (V2G) algorithm for PEV coordination that takes into consideration line capacity constraints in the distribution grid, and where energy flows both ways, from the grid in to the batteries, and from the batteries to the grid. Next, we develop a greedy-like hierarchical algorithm for management of demand response events with on/off loads. Finally, we introduce distributed algorithms for the optimal control of distributed energy resources, i.e., generation and storage in a microgrid. The algorithms we present are provably correct and tested in simulation. Each algorithm is assumed to work on a particular network topology, and simulation studies are carried out in order to demonstrate their convergence properties to a desired solution.

A digital prediction algorithm for a single-phase boost PFC

NASA Astrophysics Data System (ADS)

Qing, Wang; Ning, Chen; Weifeng, Sun; Shengli, Lu; Longxing, Shi

2012-12-01

A novel digital control algorithm for digital control power factor correction is presented, which is called the prediction algorithm and has a feature of a higher PF (power factor) with lower total harmonic distortion, and a faster dynamic response with the change of the input voltage or load current. For a certain system, based on the current system state parameters, the prediction algorithm can estimate the track of the output voltage and the inductor current at the next switching cycle and get a set of optimized control sequences to perfectly track the trajectory of input voltage. The proposed prediction algorithm is verified at different conditions, and computer simulation and experimental results under multi-situations confirm the effectiveness of the prediction algorithm. Under the circumstances that the input voltage is in the range of 90-265 V and the load current in the range of 20%-100%, the PF value is larger than 0.998. The startup and the recovery times respectively are about 0.1 s and 0.02 s without overshoot. The experimental results also verify the validity of the proposed method.

Comparative study of popular objective functions for damping power system oscillations in multimachine system.

PubMed

Islam, Naz Niamul; Hannan, M A; Shareef, Hussain; Mohamed, Azah; Salam, M A

2014-01-01

Power oscillation damping controller is designed in linearized model with heuristic optimization techniques. Selection of the objective function is very crucial for damping controller design by optimization algorithms. In this research, comparative analysis has been carried out to evaluate the effectiveness of popular objective functions used in power system oscillation damping. Two-stage lead-lag damping controller by means of power system stabilizers is optimized using differential search algorithm for different objective functions. Linearized model simulations are performed to compare the dominant mode's performance and then the nonlinear model is continued to evaluate the damping performance over power system oscillations. All the simulations are conducted in two-area four-machine power system to bring a detailed analysis. Investigated results proved that multiobjective D-shaped function is an effective objective function in terms of moving unstable and lightly damped electromechanical modes into stable region. Thus, D-shape function ultimately improves overall system damping and concurrently enhances power system reliability.

Analysis of Modified SMI Method for Adaptive Array Weight Control. M.S. Thesis

NASA Technical Reports Server (NTRS)

Dilsavor, Ronald Louis

1989-01-01

An adaptive array is used to receive a desired signal in the presence of weak interference signals which need to be suppressed. A modified sample matrix inversion (SMI) algorithm controls the array weights. The modification leads to increased interference suppression by subtracting a fraction of the noise power from the diagonal elements of the covariance matrix. The modified algorithm maximizes an intuitive power ratio criterion. The expected values and variances of the array weights, output powers, and power ratios as functions of the fraction and the number of snapshots are found and compared to computer simulation and real experimental array performance. Reduced-rank covariance approximations and errors in the estimated covariance are also described.

Central safety factor and β N control on NSTX-U via beam power and plasma boundary shape modification, using TRANSP for closed loop simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boyer, M. D.; Andre, R.; Gates, D. A.

The high-performance operational goals of NSTX-U will require development of advanced feedback control algorithms, including control of ßN and the safety factor profile. In this work, a novel approach to simultaneously controlling ßN and the value of the safety factor on the magnetic axis, q0, through manipulation of the plasma boundary shape and total beam power, is proposed. Simulations of the proposed scheme show promising results and motivate future experimental implementation and eventual integration into a more complex current profile control scheme planned to include actuation of individual beam powers, density, and loop voltage. As part of this work, amore » flexible framework for closed loop simulations within the high-fidelity code TRANSP was developed. The framework, used here to identify control-design-oriented models and to tune and test the proposed controller, exploits many of the predictive capabilities of TRANSP and provides a means for performing control calculations based on user-supplied data (controller matrices, target waveforms, etc.). The flexible framework should enable high-fidelity testing of a variety of control algorithms, thereby reducing the amount of expensive experimental time needed to implement new control algorithms on NSTX-U and other devices.« less

Central safety factor and βN control on NSTX-U via beam power and plasma boundary shape modification, using TRANSP for closed loop simulations

NASA Astrophysics Data System (ADS)

Boyer, M. D.; Andre, R.; Gates, D. A.; Gerhardt, S.; Goumiri, I. R.; Menard, J.

2015-05-01

The high-performance operational goals of NSTX-U will require development of advanced feedback control algorithms, including control of βN and the safety factor profile. In this work, a novel approach to simultaneously controlling βN and the value of the safety factor on the magnetic axis, q0, through manipulation of the plasma boundary shape and total beam power, is proposed. Simulations of the proposed scheme show promising results and motivate future experimental implementation and eventual integration into a more complex current profile control scheme planned to include actuation of individual beam powers, density, and loop voltage. As part of this work, a flexible framework for closed loop simulations within the high-fidelity code TRANSP was developed. The framework, used here to identify control-design-oriented models and to tune and test the proposed controller, exploits many of the predictive capabilities of TRANSP and provides a means for performing control calculations based on user-supplied data (controller matrices, target waveforms, etc). The flexible framework should enable high-fidelity testing of a variety of control algorithms, thereby reducing the amount of expensive experimental time needed to implement new control algorithms on NSTX-U and other devices.

Energy management and multi-layer control of networked microgrids

NASA Astrophysics Data System (ADS)

Zamora, Ramon

Networked microgrids is a group of neighboring microgrids that has ability to interchange power when required in order to increase reliability and resiliency. Networked microgrid can operate in different possible configurations including: islanded microgrid, a grid-connected microgrid without a tie-line converter, a grid-connected microgrid with a tie-line converter, and networked microgrids. These possible configurations and specific characteristics of renewable energy offer challenges in designing control and management algorithms for voltage, frequency and power in all possible operating scenarios. In this work, control algorithm is designed based on large-signal model that enables microgrid to operate in wide range of operating points. A combination between PI controller and feed-forward measured system responses will compensate for the changes in operating points. The control architecture developed in this work has multi-layers and the outer layer is slower than the inner layer in time response. The main responsibility of the designed controls are to regulate voltage magnitude and frequency, as well as output power of the DG(s). These local controls also integrate with a microgrid level energy management system or microgrid central controller (MGCC) for power and energy balance for. the entire microgrid in islanded, grid-connected, or networked microgid mode. The MGCC is responsible to coordinate the lower level controls to have reliable and resilient operation. In case of communication network failure, the decentralized energy management will operate locally and will activate droop control. Simulation results indicate the superiority of designed control algorithms compared to existing ones.

Development of a Microcontroller-based Battery Charge Controller for an Off-grid Photovoltaic System

NASA Astrophysics Data System (ADS)

Rina, Z. S.; Amin, N. A. M.; Hashim, M. S. M.; Majid, M. S. A.; Rojan, M. A.; Zaman, I.

2017-08-01

A development of a microcontroller-based charge controller for a 12V battery has been explained in this paper. The system is designed based on a novel algorithm to couple existing solar photovoltaic (PV) charging and main grid supply charging power source. One of the main purposes of the hybrid charge controller is to supply a continuous charging power source to the battery. Furthermore, the hybrid charge controller was developed to shorten the battery charging time taken. The algorithm is programmed in an Arduino Uno R3 microcontroller that monitors the battery voltage and generates appropriate commands for the charging power source selection. The solar energy is utilized whenever the solar irradiation is high. The main grid supply will be only consumed whenever the solar irradiation is low. This system ensures continuous charging power supply and faster charging of the battery.

Coordinated Control of Wind Turbine and Energy Storage System for Reducing Wind Power Fluctuation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Muljadi, Eduard; Kim, Chunghun; Chung, Chung Choo

This paper proposes a coordinated control of wind turbine and energy storage system (ESS). Because wind power (WP) is highly dependent on variable wind speed and could induce a severe stability problem to power system especially when the WP has high penetration level. To solve this problem, many power generation corporations or grid operators recently use the ESS. It has very quick response and good performance for reducing the impact of WP fluctuation but has high cost for its installation. Therefore, it is very important to design the control algorithm considering both ESS capacity and grid reliability. Thus, we proposemore » the control algorithm to mitigate the WP fluctuation by using the coordinated control between wind turbine and ESS considering ESS state of charge (SoC) and the WP fluctuation. From deloaded control according to WP fluctuation and ESS SoC management, we can expect the ESS lifespan expansion and improved grid reliability. The effectiveness of the proposed method is validated in MATLAB/Simulink considering power system including both wind turbine generator and conventional generators which react to system frequency deviation.« less

Reactor transient control in support of PFR/TREAT TUCOP experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burrows, D.R.; Larsen, G.R.; Harrison, L.J.

1984-01-01

Unique energy deposition and experiment control requirements posed bythe PFR/TREAT series of transient undercooling/overpower (TUCOP) experiments resulted in equally unique TREAT reactor operations. New reactor control computer algorithms were written and used with the TREAT reactor control computer system to perform such functions as early power burst generation (based on test train flow conditions), burst generation produced by a step insertion of reactivity following a controlled power ramp, and shutdown (SCRAM) initiators based on both test train conditions and energy deposition. Specialized hardware was constructed to simulate test train inputs to the control computer system so that computer algorithms couldmore » be tested in real time without irradiating the experiment.« less

Asymmetrical booster ascent guidance and control system design study. Volume 5: Space shuttle powered explicit guidance. [space shuttle development

NASA Technical Reports Server (NTRS)

Jaggers, R. F.

1974-01-01

An optimum powered explicit guidance algorithm capable of handling all space shuttle exoatospheric maneuvers is presented. The theoretical and practical basis for the currently baselined space shuttle powered flight guidance equations and logic is documented. Detailed flow diagrams for implementing the steering computations for all shuttle phases, including powered return to launch site (RTLS) abort, are also presented. Derivation of the powered RTLS algorithm is provided, as well as detailed flow diagrams for implementing the option. The flow diagrams and equations are compatible with the current powered flight documentation.

A real time microcomputer implementation of sensor failure detection for turbofan engines

NASA Technical Reports Server (NTRS)

Delaat, John C.; Merrill, Walter C.

1989-01-01

An algorithm was developed which detects, isolates, and accommodates sensor failures using analytical redundancy. The performance of this algorithm was demonstrated on a full-scale F100 turbofan engine. The algorithm was implemented in real-time on a microprocessor-based controls computer which includes parallel processing and high order language programming. Parallel processing was used to achieve the required computational power for the real-time implementation. High order language programming was used in order to reduce the programming and maintenance costs of the algorithm implementation software. The sensor failure algorithm was combined with an existing multivariable control algorithm to give a complete control implementation with sensor analytical redundancy. The real-time microprocessor implementation of the algorithm which resulted in the successful completion of the algorithm engine demonstration, is described.

IDMA-Based MAC Protocol for Satellite Networks with Consideration on Channel Quality

PubMed Central

2014-01-01

In order to overcome the shortcomings of existing medium access control (MAC) protocols based on TDMA or CDMA in satellite networks, interleave division multiple access (IDMA) technique is introduced into satellite communication networks. Therefore, a novel wide-band IDMA MAC protocol based on channel quality is proposed in this paper, consisting of a dynamic power allocation algorithm, a rate adaptation algorithm, and a call admission control (CAC) scheme. Firstly, the power allocation algorithm combining the technique of IDMA SINR-evolution and channel quality prediction is developed to guarantee high power efficiency even in terrible channel conditions. Secondly, the effective rate adaptation algorithm, based on accurate channel information per timeslot and by the means of rate degradation, can be realized. What is more, based on channel quality prediction, the CAC scheme, combining the new power allocation algorithm, rate scheduling, and buffering strategies together, is proposed for the emerging IDMA systems, which can support a variety of traffic types, and offering quality of service (QoS) requirements corresponding to different priority levels. Simulation results show that the new wide-band IDMA MAC protocol can make accurate estimation of available resource considering the effect of multiuser detection (MUD) and QoS requirements of multimedia traffic, leading to low outage probability as well as high overall system throughput. PMID:25126592

Real-Time Load-Side Control of Electric Power Systems

NASA Astrophysics Data System (ADS)

Zhao, Changhong

Two trends are emerging from modern electric power systems: the growth of renewable (e.g., solar and wind) generation, and the integration of information technologies and advanced power electronics. The former introduces large, rapid, and random fluctuations in power supply, demand, frequency, and voltage, which become a major challenge for real-time operation of power systems. The latter creates a tremendous number of controllable intelligent endpoints such as smart buildings and appliances, electric vehicles, energy storage devices, and power electronic devices that can sense, compute, communicate, and actuate. Most of these endpoints are distributed on the load side of power systems, in contrast to traditional control resources such as centralized bulk generators. This thesis focuses on controlling power systems in real time, using these load side resources. Specifically, it studies two problems. (1) Distributed load-side frequency control: We establish a mathematical framework to design distributed frequency control algorithms for flexible electric loads. In this framework, we formulate a category of optimization problems, called optimal load control (OLC), to incorporate the goals of frequency control, such as balancing power supply and demand, restoring frequency to its nominal value, restoring inter-area power flows, etc., in a way that minimizes total disutility for the loads to participate in frequency control by deviating from their nominal power usage. By exploiting distributed algorithms to solve OLC and analyzing convergence of these algorithms, we design distributed load-side controllers and prove stability of closed-loop power systems governed by these controllers. This general framework is adapted and applied to different types of power systems described by different models, or to achieve different levels of control goals under different operation scenarios. We first consider a dynamically coherent power system which can be equivalently modeled with a single synchronous machine. We then extend our framework to a multi-machine power network, where we consider primary and secondary frequency controls, linear and nonlinear power flow models, and the interactions between generator dynamics and load control. (2) Two-timescale voltage control: The voltage of a power distribution system must be maintained closely around its nominal value in real time, even in the presence of highly volatile power supply or demand. For this purpose, we jointly control two types of reactive power sources: a capacitor operating at a slow timescale, and a power electronic device, such as a smart inverter or a D-STATCOM, operating at a fast timescale. Their control actions are solved from optimal power flow problems at two timescales. Specifically, the slow-timescale problem is a chance-constrained optimization, which minimizes power loss and regulates the voltage at the current time instant while limiting the probability of future voltage violations due to stochastic changes in power supply or demand. This control framework forms the basis of an optimal sizing problem, which determines the installation capacities of the control devices by minimizing the sum of power loss and capital cost. We develop computationally efficient heuristics to solve the optimal sizing problem and implement real-time control. Numerical experiments show that the proposed sizing and control schemes significantly improve the reliability of voltage control with a moderate increase in cost.

Decoupled Modulation Control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Shaobu; Huang, Renke; Huang, Zhenyu

The objective of this research work is to develop decoupled modulation control methods for damping inter-area oscillations with low frequencies, so the damping control can be more effective and easier to design with less interference among different oscillation modes in the power system. A signal-decoupling algorithm was developed that can enable separation of multiple oscillation frequency contents and extraction of a “pure” oscillation frequency mode that are fed into Power System Stabilizers (PSSs) as the modulation input signals. As a result, instead of introducing interferences between different oscillation modes from the traditional approaches, the output of the new PSS modulationmore » control signal mainly affects only one oscillation mode of interest. The new decoupled modulation damping control algorithm has been successfully developed and tested on the standard IEEE 4-machine 2-area test system and a minniWECC system. The results are compared against traditional modulation controls, which demonstrates the validity and effectiveness of the newly-developed decoupled modulation damping control algorithm.« less

The architecture of adaptive neural network based on a fuzzy inference system for implementing intelligent control in photovoltaic systems

NASA Astrophysics Data System (ADS)

Gimazov, R.; Shidlovskiy, S.

2018-05-01

In this paper, we consider the architecture of the algorithm for extreme regulation in the photovoltaic system. An algorithm based on an adaptive neural network with fuzzy inference is proposed. The implementation of such an algorithm not only allows solving a number of problems in existing algorithms for extreme power regulation of photovoltaic systems, but also creates a reserve for the creation of a universal control system for a photovoltaic system.

Study on improved Ip-iq APF control algorithm and its application in micro grid

NASA Astrophysics Data System (ADS)

Xie, Xifeng; Shi, Hua; Deng, Haiyingv

2018-01-01

In order to enhance the tracking velocity and accuracy of harmonic detection by ip-iq algorithm, a novel ip-iq control algorithm based on the Instantaneous reactive power theory is presented, the improved algorithm adds the lead correction link to adjust the zero point of the detection system, the Fuzzy Self-Tuning Adaptive PI control is introduced to dynamically adjust the DC-link Voltage, which meets the requirement of the harmonic compensation of the micro grid. Simulation and experimental results verify the proposed method is feasible and effective in micro grid.

Design and simulation of sensor networks for tracking Wifi users in outdoor urban environments

NASA Astrophysics Data System (ADS)

Thron, Christopher; Tran, Khoi; Smith, Douglas; Benincasa, Daniel

2017-05-01

We present a proof-of-concept investigation into the use of sensor networks for tracking of WiFi users in outdoor urban environments. Sensors are fixed, and are capable of measuring signal power from users' WiFi devices. We derive a maximum likelihood estimate for user location based on instantaneous sensor power measurements. The algorithm takes into account the effects of power control, and is self-calibrating in that the signal power model used by the location algorithm is adjusted and improved as part of the operation of the network. Simulation results to verify the system's performance are presented. The simulation scenario is based on a 1.5 km2 area of lower Manhattan, The self-calibration mechanism was verified for initial rms (root mean square) errors of up to 12 dB in the channel power estimates: rms errors were reduced by over 60% in 300 track-hours, in systems with limited power control. Under typical operating conditions with (without) power control, location rms errors are about 8.5 (5) meters with 90% accuracy within 9 (13) meters, for both pedestrian and vehicular users. The distance error distributions for smaller distances (<30 m) are well-approximated by an exponential distribution, while the distributions for large distance errors have fat tails. The issue of optimal sensor placement in the sensor network is also addressed. We specify a linear programming algorithm for determining sensor placement for networks with reduced number of sensors. In our test case, the algorithm produces a network with 18.5% fewer sensors with comparable accuracy estimation performance. Finally, we discuss future research directions for improving the accuracy and capabilities of sensor network systems in urban environments.

An Adaptive Impedance Matching Network with Closed Loop Control Algorithm for Inductive Wireless Power Transfer

PubMed Central

Miao, Zhidong; Liu, Dake

2017-01-01

For an inductive wireless power transfer (IWPT) system, maintaining a reasonable power transfer efficiency and a stable output power are two most challenging design issues, especially when coil distance varies. To solve these issues, this paper presents a novel adaptive impedance matching network (IMN) for IWPT system. In our adaptive IMN IWPT system, the IMN is automatically reconfigured to keep matching with the coils and to adjust the output power adapting to coil distance variation. A closed loop control algorithm is used to change the capacitors continually, which can compensate mismatches and adjust output power simultaneously. The proposed adaptive IMN IWPT system is working at 125 kHz for 2 W power delivered to load. Comparing with the series resonant IWPT system and fixed IMN IWPT system, the power transfer efficiency of our system increases up to 31.79% and 60% when the coupling coefficient varies in a large range from 0.05 to 0.8 for 2 W output power. PMID:28763011

An Adaptive Impedance Matching Network with Closed Loop Control Algorithm for Inductive Wireless Power Transfer.

PubMed

Miao, Zhidong; Liu, Dake; Gong, Chen

2017-08-01

For an inductive wireless power transfer (IWPT) system, maintaining a reasonable power transfer efficiency and a stable output power are two most challenging design issues, especially when coil distance varies. To solve these issues, this paper presents a novel adaptive impedance matching network (IMN) for IWPT system. In our adaptive IMN IWPT system, the IMN is automatically reconfigured to keep matching with the coils and to adjust the output power adapting to coil distance variation. A closed loop control algorithm is used to change the capacitors continually, which can compensate mismatches and adjust output power simultaneously. The proposed adaptive IMN IWPT system is working at 125 kHz for 2 W power delivered to load. Comparing with the series resonant IWPT system and fixed IMN IWPT system, the power transfer efficiency of our system increases up to 31.79% and 60% when the coupling coefficient varies in a large range from 0.05 to 0.8 for 2 W output power.

The application of immune genetic algorithm in main steam temperature of PID control of BP network

NASA Astrophysics Data System (ADS)

Li, Han; Zhen-yu, Zhang

In order to overcome the uncertainties, large delay, large inertia and nonlinear property of the main steam temperature controlled object in the power plant, a neural network intelligent PID control system based on immune genetic algorithm and BP neural network is designed. Using the immune genetic algorithm global search optimization ability and good convergence, optimize the weights of the neural network, meanwhile adjusting PID parameters using BP network. The simulation result shows that the system is superior to conventional PID control system in the control of quality and robustness.

Protective and control relays as coal-mine power-supply ACS subsystem

NASA Astrophysics Data System (ADS)

Kostin, V. N.; Minakova, T. E.

2017-10-01

The paper presents instantaneous selective short-circuit protection for the cabling of the underground part of a coal mine and central control algorithms as a Coal-Mine Power-Supply ACS Subsystem. In order to improve the reliability of electricity supply and reduce the mining equipment down-time, a dual channel relay protection and central control system is proposed as a subsystem of the coal-mine power-supply automated control system (PS ACS).

Available Transfer Capability Determination Using Hybrid Evolutionary Algorithm

NASA Astrophysics Data System (ADS)

Jirapong, Peeraool; Ongsakul, Weerakorn

2008-10-01

This paper proposes a new hybrid evolutionary algorithm (HEA) based on evolutionary programming (EP), tabu search (TS), and simulated annealing (SA) to determine the available transfer capability (ATC) of power transactions between different control areas in deregulated power systems. The optimal power flow (OPF)-based ATC determination is used to evaluate the feasible maximum ATC value within real and reactive power generation limits, line thermal limits, voltage limits, and voltage and angle stability limits. The HEA approach simultaneously searches for real power generations except slack bus in a source area, real power loads in a sink area, and generation bus voltages to solve the OPF-based ATC problem. Test results on the modified IEEE 24-bus reliability test system (RTS) indicate that ATC determination by the HEA could enhance ATC far more than those from EP, TS, hybrid TS/SA, and improved EP (IEP) algorithms, leading to an efficient utilization of the existing transmission system.

Efficient Pricing Technique for Resource Allocation Problem in Downlink OFDM Cognitive Radio Networks

NASA Astrophysics Data System (ADS)

Abdulghafoor, O. B.; Shaat, M. M. R.; Ismail, M.; Nordin, R.; Yuwono, T.; Alwahedy, O. N. A.

2017-05-01

In this paper, the problem of resource allocation in OFDM-based downlink cognitive radio (CR) networks has been proposed. The purpose of this research is to decrease the computational complexity of the resource allocation algorithm for downlink CR network while concerning the interference constraint of primary network. The objective has been secured by adopting pricing scheme to develop power allocation algorithm with the following concerns: (i) reducing the complexity of the proposed algorithm and (ii) providing firm power control to the interference introduced to primary users (PUs). The performance of the proposed algorithm is tested for OFDM- CRNs. The simulation results show that the performance of the proposed algorithm approached the performance of the optimal algorithm at a lower computational complexity, i.e., O(NlogN), which makes the proposed algorithm suitable for more practical applications.

MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm

PubMed Central

Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L.

2014-01-01

The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339

Improving maximum power point tracking of partially shaded photovoltaic system by using IPSO-BELBIC

NASA Astrophysics Data System (ADS)

Al-Alim El-Garhy, M. Abd; Mubarak, R. I.; El-Bably, M.

2017-08-01

Solar photovoltaic (PV) arrays in remote applications are often related to the rapid changes in the partial shading pattern. Rapid changes of the partial shading pattern make the tracking of maximum power point (MPP) of the global peak through the local ones too difficult. An essential need to make a fast and efficient algorithm to detect the peaks values which always vary as the sun irradiance changes. This paper presents two algorithms based on the improved particle swarm optimization technique one of them with PID controller (IPSO-PID), and the other one with Brain Emotional Learning Based Intelligent Controller (IPSO-BELBIC). These techniques improve the maximum power point (MPP) tracking capabilities for photovoltaic (PV) system under partial shading circumstances. The main aim of these improved algorithms is to accelerate the velocity of IPSO to reach to (MPP) and increase its efficiency. These algorithms also improve the tracking time under complex irradiance conditions. Based on these conditions, the tracking time of these presented techniques improves to 2 msec, with an efficiency of 100%.

Data-driven gradient algorithm for high-precision quantum control

NASA Astrophysics Data System (ADS)

Wu, Re-Bing; Chu, Bing; Owens, David H.; Rabitz, Herschel

2018-04-01

In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., grape) are broadly used for implementing high-precision quantum gates, but their performance is often hindered by deterministic or random errors in the system model and the control electronics. In this paper, we show that grape can be taught to be more effective by jointly learning from the design model and the experimental data obtained from process tomography. The resulting data-driven gradient optimization algorithm (d-grape) can in principle correct all deterministic gate errors, with a mild efficiency loss. The d-grape algorithm may become more powerful with broadband controls that involve a large number of control parameters, while other algorithms usually slow down due to the increased size of the search space. These advantages are demonstrated by simulating the implementation of a two-qubit controlled-not gate.

Modeling and analysis of solar distributed generation

NASA Astrophysics Data System (ADS)

Ortiz Rivera, Eduardo Ivan

Recent changes in the global economy are creating a big impact in our daily life. The price of oil is increasing and the number of reserves are less every day. Also, dramatic demographic changes are impacting the viability of the electric infrastructure and ultimately the economic future of the industry. These are some of the reasons that many countries are looking for alternative energy to produce electric energy. The most common form of green energy in our daily life is solar energy. To convert solar energy into electrical energy is required solar panels, dc-dc converters, power control, sensors, and inverters. In this work, a photovoltaic module, PVM, model using the electrical characteristics provided by the manufacturer data sheet is presented for power system applications. Experimental results from testing are showed, verifying the proposed PVM model. Also in this work, three maximum power point tracker, MPPT, algorithms would be presented to obtain the maximum power from a PVM. The first MPPT algorithm is a method based on the Rolle's and Lagrange's Theorems and can provide at least an approximate answer to a family of transcendental functions that cannot be solved using differential calculus. The second MPPT algorithm is based on the approximation of the proposed PVM model using fractional polynomials where the shape, boundary conditions and performance of the proposed PVM model are satisfied. The third MPPT algorithm is based in the determination of the optimal duty cycle for a dc-dc converter and the previous knowledge of the load or load matching conditions. Also, four algorithms to calculate the effective irradiance level and temperature over a photovoltaic module are presented in this work. The main reasons to develop these algorithms are for monitoring climate conditions, the elimination of temperature and solar irradiance sensors, reductions in cost for a photovoltaic inverter system, and development of new algorithms to be integrated with maximum power point tracking algorithms. Finally, several PV power applications will be presented like circuit analysis for a load connected to two different PV arrays, speed control for a do motor connected to a PVM, and a novel single phase photovoltaic inverter system using the Z-source converter.

Power maximization of a point absorber wave energy converter using improved model predictive control

NASA Astrophysics Data System (ADS)

Milani, Farideh; Moghaddam, Reihaneh Kardehi

2017-08-01

This paper considers controlling and maximizing the absorbed power of wave energy converters for irregular waves. With respect to physical constraints of the system, a model predictive control is applied. Irregular waves' behavior is predicted by Kalman filter method. Owing to the great influence of controller parameters on the absorbed power, these parameters are optimized by imperialist competitive algorithm. The results illustrate the method's efficiency in maximizing the extracted power in the presence of unknown excitation force which should be predicted by Kalman filter.

Application of Static Var Compensator (SVC) With PI Controller for Grid Integration of Wind Farm Using Harmony Search

NASA Astrophysics Data System (ADS)

Keshta, H. E.; Ali, A. A.; Saied, E. M.; Bendary, F. M.

2016-10-01

Large-scale integration of wind turbine generators (WTGs) may have significant impacts on power system operation with respect to system frequency and bus voltages. This paper studies the effect of Static Var Compensator (SVC) connected to wind energy conversion system (WECS) on voltage profile and the power generated from the induction generator (IG) in wind farm. Also paper presents, a dynamic reactive power compensation using Static Var Compensator (SVC) at the a point of interconnection of wind farm while static compensation (Fixed Capacitor Bank) is unable to prevent voltage collapse. Moreover, this paper shows that using advanced optimization techniques based on artificial intelligence (AI) such as Harmony Search Algorithm (HS) and Self-Adaptive Global Harmony Search Algorithm (SGHS) instead of a Conventional Control Method to tune the parameters of PI controller for SVC and pitch angle. Also paper illustrates that the performance of the system with controllers based on AI is improved under different operating conditions. MATLAB/Simulink based simulation is utilized to demonstrate the application of SVC in wind farm integration. It is also carried out to investigate the enhancement in performance of the WECS achieved with a PI Controller tuned by Harmony Search Algorithm as compared to a Conventional Control Method.

Hybrid robust predictive optimization method of power system dispatch

DOEpatents

Chandra, Ramu Sharat [Niskayuna, NY; Liu, Yan [Ballston Lake, NY; Bose, Sumit [Niskayuna, NY; de Bedout, Juan Manuel [West Glenville, NY

2011-08-02

A method of power system dispatch control solves power system dispatch problems by integrating a larger variety of generation, load and storage assets, including without limitation, combined heat and power (CHP) units, renewable generation with forecasting, controllable loads, electric, thermal and water energy storage. The method employs a predictive algorithm to dynamically schedule different assets in order to achieve global optimization and maintain the system normal operation.

Power inverter implementing phase skipping control

DOEpatents

Somani, Utsav; Amirahmadi, Ahmadreza; Jourdan, Charles; Batarseh, Issa

2016-10-18

A power inverter includes a DC/AC inverter having first, second and third phase circuitry coupled to receive power from a power source. A controller is coupled to a driver for each of the first, second and third phase circuitry (control input drivers). The controller includes an associated memory storing a phase skipping control algorithm, wherein the controller is coupled to receive updating information including a power level generated by the power source. The drivers are coupled to control inputs of the first, second and third phase circuitry, where the drivers are configured for receiving phase skipping control signals from the controller and outputting mode selection signals configured to dynamically select an operating mode for the DC/AC inverter from a Normal Control operation and a Phase Skipping Control operation which have different power injection patterns through the first, second and third phase circuitry depending upon the power level.

Advanced CHP Control Algorithms: Scope Specification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Katipamula, Srinivas; Brambley, Michael R.

2006-04-28

The primary objective of this multiyear project is to develop algorithms for combined heat and power systems to ensure optimal performance, increase reliability, and lead to the goal of clean, efficient, reliable and affordable next generation energy systems.

Hybrid power system intelligent operation and protection involving distributed architectures and pulsed loads

NASA Astrophysics Data System (ADS)

Mohamed, Ahmed

Efficient and reliable techniques for power delivery and utilization are needed to account for the increased penetration of renewable energy sources in electric power systems. Such methods are also required for current and future demands of plug-in electric vehicles and high-power electronic loads. Distributed control and optimal power network architectures will lead to viable solutions to the energy management issue with high level of reliability and security. This dissertation is aimed at developing and verifying new techniques for distributed control by deploying DC microgrids, involving distributed renewable generation and energy storage, through the operating AC power system. To achieve the findings of this dissertation, an energy system architecture was developed involving AC and DC networks, both with distributed generations and demands. The various components of the DC microgrid were designed and built including DC-DC converters, voltage source inverters (VSI) and AC-DC rectifiers featuring novel designs developed by the candidate. New control techniques were developed and implemented to maximize the operating range of the power conditioning units used for integrating renewable energy into the DC bus. The control and operation of the DC microgrids in the hybrid AC/DC system involve intelligent energy management. Real-time energy management algorithms were developed and experimentally verified. These algorithms are based on intelligent decision-making elements along with an optimization process. This was aimed at enhancing the overall performance of the power system and mitigating the effect of heavy non-linear loads with variable intensity and duration. The developed algorithms were also used for managing the charging/discharging process of plug-in electric vehicle emulators. The protection of the proposed hybrid AC/DC power system was studied. Fault analysis and protection scheme and coordination, in addition to ideas on how to retrofit currently available protection concepts and devices for AC systems in a DC network, were presented. A study was also conducted on the effect of changing the distribution architecture and distributing the storage assets on the various zones of the network on the system's dynamic security and stability. A practical shipboard power system was studied as an example of a hybrid AC/DC power system involving pulsed loads. Generally, the proposed hybrid AC/DC power system, besides most of the ideas, controls and algorithms presented in this dissertation, were experimentally verified at the Smart Grid Testbed, Energy Systems Research Laboratory. All the developments in this dissertation were experimentally verified at the Smart Grid Testbed.

Design and control of the MINDWALKER exoskeleton.

PubMed

Wang, Shiqian; Wang, Letian; Meijneke, Cory; van Asseldonk, Edwin; Hoellinger, Thomas; Cheron, Guy; Ivanenko, Yuri; La Scaleia, Valentina; Sylos-Labini, Francesca; Molinari, Marco; Tamburella, Federica; Pisotta, Iolanda; Thorsteinsson, Freygardur; Ilzkovitz, Michel; Gancet, Jeremi; Nevatia, Yashodhan; Hauffe, Ralf; Zanow, Frank; van der Kooij, Herman

2015-03-01

Powered exoskeletons can empower paraplegics to stand and walk. Actively controlled hip ab/adduction (HAA) is needed for weight shift and for lateral foot placement to support dynamic balance control and to counteract disturbances in the frontal plane. Here, we describe the design, control, and preliminary evaluation of a novel exoskeleton, MINDWALKER. Besides powered hip flexion/extension and knee flexion/extension, it also has powered HAA. Each of the powered joints has a series elastic actuator, which can deliver 100 Nm torque and 1 kW power. A finite-state machine based controller provides gait assistance in both the sagittal and frontal planes. State transitions, such as stepping, can be triggered by the displacement of the Center of Mass (CoM). A novel step-width adaptation algorithm was proposed to stabilize lateral balance. We tested this exoskeleton on both healthy subjects and paraplegics. Experimental results showed that all users could successfully trigger steps by CoM displacement. The step-width adaptation algorithm could actively counteract disturbances, such as pushes. With the current implementations, stable walking without crutches has been achieved for healthy subjects but not yet for SCI paraplegics. More research and development is needed to improve the gait stability.

Synchronization Algorithms for Co-Simulation of Power Grid and Communication Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ciraci, Selim; Daily, Jeffrey A.; Agarwal, Khushbu

2014-09-11

The ongoing modernization of power grids consists of integrating them with communication networks in order to achieve robust and resilient control of grid operations. To understand the operation of the new smart grid, one approach is to use simulation software. Unfortunately, current power grid simulators at best utilize inadequate approximations to simulate communication networks, if at all. Cooperative simulation of specialized power grid and communication network simulators promises to more accurately reproduce the interactions of real smart grid deployments. However, co-simulation is a challenging problem. A co-simulation must manage the exchange of informa- tion, including the synchronization of simulator clocks,more » between all simulators while maintaining adequate computational perfor- mance. This paper describes two new conservative algorithms for reducing the overhead of time synchronization, namely Active Set Conservative and Reactive Conservative. We provide a detailed analysis of their performance characteristics with respect to the current state of the art including both conservative and optimistic synchronization algorithms. In addition, we provide guidelines for selecting the appropriate synchronization algorithm based on the requirements of the co-simulation. The newly proposed algorithms are shown to achieve as much as 14% and 63% im- provement, respectively, over the existing conservative algorithm.« less

Subsonic flight test evaluation of a performance seeking control algorithm on an F-15 airplane

NASA Technical Reports Server (NTRS)

Gilyard, Glenn B.; Orme, John S.

1992-01-01

The subsonic flight test evaluation phase of the NASA F-15 (powered by F 100 engines) performance seeking control program was completed for single-engine operation at part- and military-power settings. The subsonic performance seeking control algorithm optimizes the quasi-steady-state performance of the propulsion system for three modes of operation. The minimum fuel flow mode minimizes fuel consumption. The minimum thrust mode maximizes thrust at military power. Decreases in thrust-specific fuel consumption of 1 to 2 percent were measured in the minimum fuel flow mode; these fuel savings are significant, especially for supersonic cruise aircraft. Decreases of up to approximately 100 degree R in fan turbine inlet temperature were measured in the minimum temperature mode. Temperature reductions of this magnitude would more than double turbine life if inlet temperature was the only life factor. Measured thrust increases of up to approximately 15 percent in the maximum thrust mode cause substantial increases in aircraft acceleration. The system dynamics of the closed-loop algorithm operation were good. The subsonic flight phase has validated the performance seeking control technology, which can significantly benefit the next generation of fighter and transport aircraft.

Development of a solar-powered electric bicycle in bike sharing transportation system

NASA Astrophysics Data System (ADS)

Adhisuwignjo, S.; Siradjuddin, I.; Rifa'i, M.; Putri, R. I.

2017-06-01

The increasing mobility has directly led to deteriorating traffic conditions, extra fuel consumption, increasing automobile exhaust emissions, air pollution and lowering quality of life. Apart from being clean, cheap and equitable mode of transport for short-distance journeys, cycling can potentially offer solutions to the problem of urban mobility. Many cities have tried promoting cycling particularly through the implementation of bike-sharing. Apparently the fourth generation bikesharing system has been promoted utilizing electric bicycles which considered as a clean technology implementation. Utilization of solar power is probably the development keys in the fourth generation bikesharing system and will become the standard in bikesharing system in the future. Electric bikes use batteries as a source of energy, thus they require a battery charger system which powered from the solar cells energy. This research aims to design and implement electric bicycle battery charging system with solar energy sources using fuzzy logic algorithm. It is necessary to develop an electric bicycle battery charging system with solar energy sources using fuzzy logic algorithm. The study was conducted by means of experimental method which includes the design, manufacture and testing controller systems. The designed fuzzy algorithm have been planted in EEPROM microcontroller ATmega8535. The charging current was set at 1.2 Amperes and the full charged battery voltage was observed to be 40 Volts. The results showed a fuzzy logic controller was able to maintain the charging current of 1.2 Ampere with an error rate of less than 5% around the set point. The process of charging electric bike lead acid batteries from empty to fully charged was 5 hours. In conclusion, the development of solar-powered electric bicycle controlled using fuzzy logic controller can keep the battery charging current in solar-powered electric bicycle to remain stable. This shows that the fuzzy algorithm can be used as a controller in the process of charging for a solar electric bicycle.

Operation of Power Grids with High Penetration of Wind Power

NASA Astrophysics Data System (ADS)

Al-Awami, Ali Taleb

The integration of wind power into the power grid poses many challenges due to its highly uncertain nature. This dissertation involves two main components related to the operation of power grids with high penetration of wind energy: wind-thermal stochastic dispatch and wind-thermal coordinated bidding in short-term electricity markets. In the first part, a stochastic dispatch (SD) algorithm is proposed that takes into account the stochastic nature of the wind power output. The uncertainty associated with wind power output given the forecast is characterized using conditional probability density functions (CPDF). Several functions are examined to characterize wind uncertainty including Beta, Weibull, Extreme Value, Generalized Extreme Value, and Mixed Gaussian distributions. The unique characteristics of the Mixed Gaussian distribution are then utilized to facilitate the speed of convergence of the SD algorithm. A case study is carried out to evaluate the effectiveness of the proposed algorithm. Then, the SD algorithm is extended to simultaneously optimize the system operating costs and emissions. A modified multi-objective particle swarm optimization algorithm is suggested to identify the Pareto-optimal solutions defined by the two conflicting objectives. A sensitivity analysis is carried out to study the effect of changing load level and imbalance cost factors on the Pareto front. In the second part of this dissertation, coordinated trading of wind and thermal energy is proposed to mitigate risks due to those uncertainties. The problem of wind-thermal coordinated trading is formulated as a mixed-integer stochastic linear program. The objective is to obtain the optimal tradeoff bidding strategy that maximizes the total expected profits while controlling trading risks. For risk control, a weighted term of the conditional value at risk (CVaR) is included in the objective function. The CVaR aims to maximize the expected profits of the least profitable scenarios, thus improving trading risk control. A case study comparing coordinated with uncoordinated bidding strategies depending on the trader's risk attitude is included. Simulation results show that coordinated bidding can improve the expected profits while significantly improving the CVaR.

Training Recurrent Neural Networks With the Levenberg-Marquardt Algorithm for Optimal Control of a Grid-Connected Converter.

PubMed

Fu, Xingang; Li, Shuhui; Fairbank, Michael; Wunsch, Donald C; Alonso, Eduardo

2015-09-01

This paper investigates how to train a recurrent neural network (RNN) using the Levenberg-Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected converter (GCC) using an RNN. To successfully and efficiently train an RNN using the LM algorithm, a new forward accumulation through time (FATT) algorithm is proposed to calculate the Jacobian matrix required by the LM algorithm. This paper explores how to incorporate FATT into the LM algorithm. The results show that the combination of the LM and FATT algorithms trains RNNs better than the conventional backpropagation through time algorithm. This paper presents an analytical study on the optimal control of GCCs, including theoretically ideal optimal and suboptimal controllers. To overcome the inapplicability of the optimal GCC controller under practical conditions, a new RNN controller with an improved input structure is proposed to approximate the ideal optimal controller. The performance of an ideal optimal controller and a well-trained RNN controller was compared in close to real-life power converter switching environments, demonstrating that the proposed RNN controller can achieve close to ideal optimal control performance even under low sampling rate conditions. The excellent performance of the proposed RNN controller under challenging and distorted system conditions further indicates the feasibility of using an RNN to approximate optimal control in practical applications.

Determination of power system component parameters using nonlinear dead beat estimation method

NASA Astrophysics Data System (ADS)

Kolluru, Lakshmi

Power systems are considered the most complex man-made wonders in existence today. In order to effectively supply the ever increasing demands of the consumers, power systems are required to remain stable at all times. Stability and monitoring of these complex systems are achieved by strategically placed computerized control centers. State and parameter estimation is an integral part of these facilities, as they deal with identifying the unknown states and/or parameters of the systems. Advancements in measurement technologies and the introduction of phasor measurement units (PMU) provide detailed and dynamic information of all measurements. Accurate availability of dynamic measurements provides engineers the opportunity to expand and explore various possibilities in power system dynamic analysis/control. This thesis discusses the development of a parameter determination algorithm for nonlinear power systems, using dynamic data obtained from local measurements. The proposed algorithm was developed by observing the dead beat estimator used in state space estimation of linear systems. The dead beat estimator is considered to be very effective as it is capable of obtaining the required results in a fixed number of steps. The number of steps required is related to the order of the system and the number of parameters to be estimated. The proposed algorithm uses the idea of dead beat estimator and nonlinear finite difference methods to create an algorithm which is user friendly and can determine the parameters fairly accurately and effectively. The proposed algorithm is based on a deterministic approach, which uses dynamic data and mathematical models of power system components to determine the unknown parameters. The effectiveness of the algorithm is tested by implementing it to identify the unknown parameters of a synchronous machine. MATLAB environment is used to create three test cases for dynamic analysis of the system with assumed known parameters. Faults are introduced in the virtual test systems and the dynamic data obtained in each case is analyzed and recorded. Ideally, actual measurements are to be provided to the algorithm. As the measurements are not readily available the data obtained from simulations is fed into the determination algorithm as inputs. The obtained results are then compared to the original (or assumed) values of the parameters. The results obtained suggest that the algorithm is able to determine the parameters of a synchronous machine when crisp data is available.

Automated electric power management and control for Space Station Freedom

NASA Technical Reports Server (NTRS)

Dolce, James L.; Mellor, Pamela A.; Kish, James A.

1990-01-01

A comprehensive automation design is being developed for Space Station Freedom's electric power system. It strives to increase station productivity by applying expert systems and conventional algorithms to automate power system operation. An integrated approach to the power system command and control problem is defined and used to direct technology development in: diagnosis, security monitoring and analysis, battery management, and cooperative problem-solving for resource allocation. The prototype automated power system is developed using simulations and test-beds.

Optimal coordinated voltage control in active distribution networks using backtracking search algorithm

PubMed Central

Tengku Hashim, Tengku Juhana; Mohamed, Azah

2017-01-01

The growing interest in distributed generation (DG) in recent years has led to a number of generators connected to a distribution system. The integration of DGs in a distribution system has resulted in a network known as active distribution network due to the existence of bidirectional power flow in the system. Voltage rise issue is one of the predominantly important technical issues to be addressed when DGs exist in an active distribution network. This paper presents the application of the backtracking search algorithm (BSA), which is relatively new optimisation technique to determine the optimal settings of coordinated voltage control in a distribution system. The coordinated voltage control considers power factor, on-load tap-changer and generation curtailment control to manage voltage rise issue. A multi-objective function is formulated to minimise total losses and voltage deviation in a distribution system. The proposed BSA is compared with that of particle swarm optimisation (PSO) so as to evaluate its effectiveness in determining the optimal settings of power factor, tap-changer and percentage active power generation to be curtailed. The load flow algorithm from MATPOWER is integrated in the MATLAB environment to solve the multi-objective optimisation problem. Both the BSA and PSO optimisation techniques have been tested on a radial 13-bus distribution system and the results show that the BSA performs better than PSO by providing better fitness value and convergence rate. PMID:28991919

Optimal coordinated voltage control in active distribution networks using backtracking search algorithm.

PubMed

Tengku Hashim, Tengku Juhana; Mohamed, Azah

2017-01-01

The growing interest in distributed generation (DG) in recent years has led to a number of generators connected to a distribution system. The integration of DGs in a distribution system has resulted in a network known as active distribution network due to the existence of bidirectional power flow in the system. Voltage rise issue is one of the predominantly important technical issues to be addressed when DGs exist in an active distribution network. This paper presents the application of the backtracking search algorithm (BSA), which is relatively new optimisation technique to determine the optimal settings of coordinated voltage control in a distribution system. The coordinated voltage control considers power factor, on-load tap-changer and generation curtailment control to manage voltage rise issue. A multi-objective function is formulated to minimise total losses and voltage deviation in a distribution system. The proposed BSA is compared with that of particle swarm optimisation (PSO) so as to evaluate its effectiveness in determining the optimal settings of power factor, tap-changer and percentage active power generation to be curtailed. The load flow algorithm from MATPOWER is integrated in the MATLAB environment to solve the multi-objective optimisation problem. Both the BSA and PSO optimisation techniques have been tested on a radial 13-bus distribution system and the results show that the BSA performs better than PSO by providing better fitness value and convergence rate.

Automating security monitoring and analysis for Space Station Freedom's electric power system

NASA Technical Reports Server (NTRS)

Dolce, James L.; Sobajic, Dejan J.; Pao, Yoh-Han

1990-01-01

Operating a large, space power system requires classifying the system's status and analyzing its security. Conventional algorithms are used by terrestrial electric utilities to provide such information to their dispatchers, but their application aboard Space Station Freedom will consume too much processing time. A new approach for monitoring and analysis using adaptive pattern techniques is presented. This approach yields an on-line security monitoring and analysis algorithm that is accurate and fast; and thus, it can free the Space Station Freedom's power control computers for other tasks.

Automating security monitoring and analysis for Space Station Freedom's electric power system

NASA Technical Reports Server (NTRS)

Dolce, James L.; Sobajic, Dejan J.; Pao, Yoh-Han

1990-01-01

Operating a large, space power system requires classifying the system's status and analyzing its security. Conventional algorithms are used by terrestrial electric utilities to provide such information to their dispatchers, but their application aboard Space Station Freedom will consume too much processing time. A novel approach for monitoring and analysis using adaptive pattern techniques is presented. This approach yields an on-line security monitoring and analysis algorithm that is accurate and fast; and thus, it can free the Space Station Freedom's power control computers for other tasks.

Load Frequency Control of a Two-Area Thermal-Hybrid Power System Using a Novel Quasi-Opposition Harmony Search Algorithm

NASA Astrophysics Data System (ADS)

Mahto, Tarkeshwar; Mukherjee, V.

2016-09-01

In the present work, a two-area thermal-hybrid interconnected power system, consisting of a thermal unit in one area and a hybrid wind-diesel unit in other area is considered. Capacitive energy storage (CES) and CES with static synchronous series compensator (SSSC) are connected to the studied two-area model to compensate for varying load demand, intermittent output power and area frequency oscillation. A novel quasi-opposition harmony search (QOHS) algorithm is proposed and applied to tune the various tunable parameters of the studied power system model. Simulation study reveals that inclusion of CES unit in both the areas yields superb damping performance for frequency and tie-line power deviation. From the simulation results it is further revealed that inclusion of SSSC is not viable from both technical as well as economical point of view as no considerable improvement in transient performance is noted with its inclusion in the tie-line of the studied power system model. The results presented in this paper demonstrate the potential of the proposed QOHS algorithm and show its effectiveness and robustness for solving frequency and power drift problems of the studied power systems. Binary coded genetic algorithm is taken for sake of comparison.

Lightweight Battery Charge Regulator Used to Track Solar Array Peak Power

NASA Technical Reports Server (NTRS)

Soeder, James F.; Button, Robert M.

1999-01-01

A battery charge regulator based on the series-connected boost regulator (SCBR) technology has been developed for high-voltage spacecraft applications. The SCBR regulates the solar array power during insolation to prevent battery overcharge or undercharge conditions. It can also be used to provide regulated battery output voltage to spacecraft loads if necessary. This technology uses industry-standard dc-dc converters and a unique interconnection to provide size, weight, efficiency, fault tolerance, and modularity benefits over existing systems. The high-voltage SCBR shown in the photograph has demonstrated power densities of over 1000 watts per kilogram (W/kg). Using four 150-W dc-dc converter modules, it can process 2500 W of power at 120 Vdc with a minimum input voltage of 90 Vdc. Efficiency of the SCBR was 94 to 98 percent over the entire operational range. Internally, the unit is made of two separate SCBR s, each with its own analog control circuitry, to demonstrate the modularity of the technology. The analog controllers regulate the output current and incorporate the output voltage limit with active current sharing between the two units. They also include voltage and current telemetry, on/off control, and baseplate temperature sensors. For peak power tracking, the SCBR was connected to a LabView-based data acquisition system for telemetry and control. A digital control algorithm for tracking the peak power point of a solar array was developed using the principle of matching the source impedance with the load impedance for maximum energy transfer. The algorithm was successfully demonstrated in a simulated spacecraft electrical system at the Boeing PhantomWorks High Voltage Test Facility in Seattle, Washington. The system consists of a 42-string, high-voltage solar array simulator, a 77-cell, 80-ampere-hour (A-hr) nickel-hydrogen battery, and a constant power-load module. The SCBR and the LabView control algorithm successfully tracked the solar array peak power point through various load transients, including sunlight discharge transients when the total load exceeded the maximum solar array output power.

Fuzzy Inference Based Obstacle Avoidance Control of Electric Powered Wheelchair Considering Driving Risk

NASA Astrophysics Data System (ADS)

Kiso, Atsushi; Murakami, Hiroki; Seki, Hirokazu

This paper describes a novel obstacle avoidance control scheme of electric powered wheelchairs for realizing the safe driving in various environments. The “electric powered wheelchair” which generates the driving force by electric motors is expected to be widely used as a mobility support system for elderly people and disabled people; however, the driving performance must be further improved because the number of driving accidents caused by elderly operator's narrow sight and joystick operation errors is increasing. This paper proposes a novel obstacle avoidance control scheme based on fuzzy algorithm to prevent driving accidents. The proposed control system determines the driving direction by fuzzy algorithm based on the information of the joystick operation and distance to obstacles measured by ultrasonic sensors. Fuzzy rules to determine the driving direction are designed surely to avoid passers-by and walls considering the human's intent and driving environments. Some driving experiments on the practical situations show the effectiveness of the proposed control system.

Chaos control in solar fed DC-DC boost converter by optimal parameters using nelder-mead algorithm powered enhanced BFOA

NASA Astrophysics Data System (ADS)

Sudhakar, N.; Rajasekar, N.; Akhil, Saya; Jyotheeswara Reddy, K.

2017-11-01

The boost converter is the most desirable DC-DC power converter for renewable energy applications for its favorable continuous input current characteristics. In other hand, these DC-DC converters known as practical nonlinear systems are prone to several types of nonlinear phenomena including bifurcation, quasiperiodicity, intermittency and chaos. These undesirable effects has to be controlled for maintaining normal periodic operation of the converter and to ensure the stability. This paper presents an effective solution to control the chaos in solar fed DC-DC boost converter since the converter experiences wide range of input power variation which leads to chaotic phenomena. Controlling of chaos is significantly achieved using optimal circuit parameters obtained through Nelder-Mead Enhanced Bacterial Foraging Optimization Algorithm. The optimization renders the suitable parameters in minimum computational time. The results are compared with the traditional methods. The obtained results of the proposed system ensures the operation of the converter within the controllable region.

Station Keeping of Small Outboard-Powered Boats

NASA Technical Reports Server (NTRS)

Fisher, A. D.; VanZwieten, J. H., Jr.; VanZwieten, T. S.

2010-01-01

Three station keeping controllers have been developed which work to minimize displacement of a small outboard-powered vessel from a desired location. Each of these three controllers has a common initial layer that uses fixed-gain feedback control to calculate the desired heading of the vessel. A second control layer uses a common fixed-gain feedback controller to calculate the net forward thrust, one of two algorithms for controlling engine angle (Fixed-Gain Proportional-integral-derivative (PID) or PID with Adaptively Augmented Gains), and one of two algorithms for differential throttle control (Fixed-Gain PID and PID with Adaptive Differential Throttle gains), which work together to eliminate heading error. The three selected controllers are evaluated using a numerical simulation of a 33-foot center console vessel with twin outboards that is subject to wave, wind, and current disturbances. Each controller is tested for its ability to maintain position in the presence of three sets of environmental disturbances. These algorithms were tested with current velocity of 1.5 m/s, significant wave height of 0.5 m, and wind speeds of 2, 5, and 10 m/s. These values were chosen to model conditions a small vessel may experience in the Gulf Stream off of Fort Lauderdale. The Fixed-gain PID controller progressively got worse as wind speeds increased, while the controllers using adaptive methodologies showed consistent performance over all weather conditions and decreased heading error by as much as 20%. Thus, enhanced robustness to environmental changes has been gained by using an adaptive algorithm.

Incipient fault detection and power system protection for spaceborne systems

NASA Technical Reports Server (NTRS)

Russell, B. Don; Hackler, Irene M.

1987-01-01

A program was initiated to study the feasibility of using advanced terrestrial power system protection techniques for spacecraft power systems. It was designed to enhance and automate spacecraft power distribution systems in the areas of safety, reliability and maintenance. The proposed power management/distribution system is described as well as security assessment and control, incipient and low current fault detection, and the proposed spaceborne protection system. It is noted that the intelligent remote power controller permits the implementation of digital relaying algorithms with both adaptive and programmable characteristics.

Fast-kick-off monotonically convergent algorithm for searching optimal control fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liao, Sheng-Lun; Ho, Tak-San; Rabitz, Herschel

2011-09-15

This Rapid Communication presents a fast-kick-off search algorithm for quickly finding optimal control fields in the state-to-state transition probability control problems, especially those with poorly chosen initial control fields. The algorithm is based on a recently formulated monotonically convergent scheme [T.-S. Ho and H. Rabitz, Phys. Rev. E 82, 026703 (2010)]. Specifically, the local temporal refinement of the control field at each iteration is weighted by a fractional inverse power of the instantaneous overlap of the backward-propagating wave function, associated with the target state and the control field from the previous iteration, and the forward-propagating wave function, associated with themore » initial state and the concurrently refining control field. Extensive numerical simulations for controls of vibrational transitions and ultrafast electron tunneling show that the new algorithm not only greatly improves the search efficiency but also is able to attain good monotonic convergence quality when further frequency constraints are required. The algorithm is particularly effective when the corresponding control dynamics involves a large number of energy levels or ultrashort control pulses.« less

Development of Real Time Implementation of 5/5 Rule based Fuzzy Logic Controller Shunt Active Power Filter for Power Quality Improvement

NASA Astrophysics Data System (ADS)

Puhan, Pratap Sekhar; Ray, Pravat Kumar; Panda, Gayadhar

2016-12-01

This paper presents the effectiveness of 5/5 Fuzzy rule implementation in Fuzzy Logic Controller conjunction with indirect control technique to enhance the power quality in single phase system, An indirect current controller in conjunction with Fuzzy Logic Controller is applied to the proposed shunt active power filter to estimate the peak reference current and capacitor voltage. Current Controller based pulse width modulation (CCPWM) is used to generate the switching signals of voltage source inverter. Various simulation results are presented to verify the good behaviour of the Shunt active Power Filter (SAPF) with proposed two levels Hysteresis Current Controller (HCC). For verification of Shunt Active Power Filter in real time, the proposed control algorithm has been implemented in laboratory developed setup in dSPACE platform.

Installation of automatic control at experimental breeder reactor II

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larson, H.A.; Booty, W.F.; Chick, D.R.

1985-08-01

The Experimental Breeder Reactor II (EBR-II) has been modified to permit automatic control capability. Necessary mechanical and electrical changes were made on a regular control rod position; motor, gears, and controller were replaced. A digital computer system was installed that has the programming capability for varied power profiles. The modifications permit transient testing at EBR-II. Experiments were run that increased power linearly as much as 4 MW/s (16% of initial power of 25 MW(thermal)/s), held power constant, and decreased power at a rate no slower than the increase rate. Thus the performance of the automatic control algorithm, the mechanical andmore » electrical control equipment, and the qualifications of the driver fuel for future power change experiments were all demonstrated.« less

Peak reduction for commercial buildings using energy storage

NASA Astrophysics Data System (ADS)

Chua, K. H.; Lim, Y. S.; Morris, S.

2017-11-01

Battery-based energy storage has emerged as a cost-effective solution for peak reduction due to the decrement of battery’s price. In this study, a battery-based energy storage system is developed and implemented to achieve an optimal peak reduction for commercial customers with the limited energy capacity of the energy storage. The energy storage system is formed by three bi-directional power converter rated at 5 kVA and a battery bank with capacity of 64 kWh. Three control algorithms, namely fixed-threshold, adaptive-threshold, and fuzzy-based control algorithms have been developed and implemented into the energy storage system in a campus building. The control algorithms are evaluated and compared under different load conditions. The overall experimental results show that the fuzzy-based controller is the most effective algorithm among the three controllers in peak reduction. The fuzzy-based control algorithm is capable of incorporating a priori qualitative knowledge and expertise about the load characteristic of the buildings as well as the useable energy without over-discharging the batteries.

System for computer controlled shifting of an automatic transmission

DOEpatents

Patil, Prabhakar B.

1989-01-01

In an automotive vehicle having an automatic transmission that driveably connects a power source to the driving wheels, a method to control the application of hydraulic pressure to a clutch, whose engagement produces an upshift and whose disengagement produces a downshift, the speed of the power source, and the output torque of the transmission. The transmission output shaft torque and the power source speed are the controlled variables. The commanded power source torque and commanded hydraulic pressure supplied to the clutch are the control variables. A mathematical model is formulated that describes the kinematics and dynamics of the powertrain before, during and after a gear shift. The model represents the operating characteristics of each component and the structural arrangement of the components within the transmission being controlled. Next, a close loop feedback control is developed to determine the proper control law or compensation strategy to achieve an acceptably smooth gear ratio change, one in which the output torque disturbance is kept to a minimum and the duration of the shift is minimized. Then a computer algorithm simulating the shift dynamics employing the mathematical model is used to study the effects of changes in the values of the parameters established from a closed loop control of the clutch hydraulic and the power source torque on the shift quality. This computer simulation is used also to establish possible shift control strategies. The shift strategies determine from the prior step are reduced to an algorithm executed by a computer to control the operation of the power source and the transmission.

Closed loop computer control for an automatic transmission

DOEpatents

Patil, Prabhakar B.

1989-01-01

In an automotive vehicle having an automatic transmission that driveably connects a power source to the driving wheels, a method to control the application of hydraulic pressure to a clutch, whose engagement produces an upshift and whose disengagement produces a downshift, the speed of the power source, and the output torque of the transmission. The transmission output shaft torque and the power source speed are the controlled variables. The commanded power source torque and commanded hydraulic pressure supplied to the clutch are the control variables. A mathematical model is formulated that describes the kinematics and dynamics of the powertrain before, during and after a gear shift. The model represents the operating characteristics of each component and the structural arrangement of the components within the transmission being controlled. Next, a close loop feedback control is developed to determine the proper control law or compensation strategy to achieve an acceptably smooth gear ratio change, one in which the output torque disturbance is kept to a minimum and the duration of the shift is minimized. Then a computer algorithm simulating the shift dynamics employing the mathematical model is used to study the effects of changes in the values of the parameters established from a closed loop control of the clutch hydraulic and the power source torque on the shift quality. This computer simulation is used also to establish possible shift control strategies. The shift strategies determined from the prior step are reduced to an algorithm executed by a computer to control the operation of the power source and the transmission.

Recovery Act: Energy Efficiency of Data Networks through Rate Adaptation (EEDNRA) - Final Technical Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matthew Andrews; Spyridon Antonakopoulos; Steve Fortune

2011-07-12

This Concept Definition Study focused on developing a scientific understanding of methods to reduce energy consumption in data networks using rate adaptation. Rate adaptation is a collection of techniques that reduce energy consumption when traffic is light, and only require full energy when traffic is at full provisioned capacity. Rate adaptation is a very promising technique for saving energy: modern data networks are typically operated at average rates well below capacity, but network equipment has not yet been designed to incorporate rate adaptation. The Study concerns packet-switching equipment, routers and switches; such equipment forms the backbone of the modern Internet.more » The focus of the study is on algorithms and protocols that can be implemented in software or firmware to exploit hardware power-control mechanisms. Hardware power-control mechanisms are widely used in the computer industry, and are beginning to be available for networking equipment as well. Network equipment has different performance requirements than computer equipment because of the very fast rate of packet arrival; hence novel power-control algorithms are required for networking. This study resulted in five published papers, one internal report, and two patent applications, documented below. The specific technical accomplishments are the following: • A model for the power consumption of switching equipment used in service-provider telecommunication networks as a function of operating state, and measured power-consumption values for typical current equipment. • An algorithm for use in a router that adapts packet processing rate and hence power consumption to traffic load while maintaining performance guarantees on delay and throughput. • An algorithm that performs network-wide traffic routing with the objective of minimizing energy consumption, assuming that routers have less-than-ideal rate adaptivity. • An estimate of the potential energy savings in service-provider networks using feasibly-implementable rate adaptivity. • A buffer-management algorithm that is designed to reduce the size of router buffers, and hence energy consumed. • A packet-scheduling algorithm designed to minimize packet-processing energy requirements. Additional research is recommended in at least two areas: further exploration of rate-adaptation in network switching equipment, including incorporation of rate-adaptation in actual hardware, allowing experimentation in operational networks; and development of control protocols that allow parts of networks to be shut down while minimizing disruption to traffic flow in the network. The research is an integral part of a large effort within Bell Laboratories, Alcatel-Lucent, aimed at dramatic improvements in the energy efficiency of telecommunication networks. This Study did not explicitly consider any commercialization opportunities.« less

Cost-effective solutions to maintaining smart grid reliability

NASA Astrophysics Data System (ADS)

Qin, Qiu

As the aging power systems are increasingly working closer to the capacity and thermal limits, maintaining an sufficient reliability has been of great concern to the government agency, utility companies and users. This dissertation focuses on improving the reliability of transmission and distribution systems. Based on the wide area measurements, multiple model algorithms are developed to diagnose transmission line three-phase short to ground faults in the presence of protection misoperations. The multiple model algorithms utilize the electric network dynamics to provide prompt and reliable diagnosis outcomes. Computational complexity of the diagnosis algorithm is reduced by using a two-step heuristic. The multiple model algorithm is incorporated into a hybrid simulation framework, which consist of both continuous state simulation and discrete event simulation, to study the operation of transmission systems. With hybrid simulation, line switching strategy for enhancing the tolerance to protection misoperations is studied based on the concept of security index, which involves the faulted mode probability and stability coverage. Local measurements are used to track the generator state and faulty mode probabilities are calculated in the multiple model algorithms. FACTS devices are considered as controllers for the transmission system. The placement of FACTS devices into power systems is investigated with a criterion of maintaining a prescribed level of control reconfigurability. Control reconfigurability measures the small signal combined controllability and observability of a power system with an additional requirement on fault tolerance. For the distribution systems, a hierarchical framework, including a high level recloser allocation scheme and a low level recloser placement scheme, is presented. The impacts of recloser placement on the reliability indices is analyzed. Evaluation of reliability indices in the placement process is carried out via discrete event simulation. The reliability requirements are described with probabilities and evaluated from the empirical distributions of reliability indices.

Coordinated train control and energy management control strategies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gordon, S.P.; Lehrer, D.G.

1998-05-01

The Bay Area Rapid Transit (BART) system, in collaboration with Hughes Aircraft Company and Harmon Industries, as in the process of developing an Advanced Automatic Train Control (AATC) system to replace the current fixed-block automatic system. In the long run, the AATC system is expected to not only allow for safe short headway operation, but also to facilitate coordinated train control and energy management. This new system will employ spread spectrum radios, installed onboard trains, at wayside locations, and at control stations, to determine train locations and reliably transfer control information. Sandia National Laboratories has worked cooperatively with BART tomore » develop a simulator of the train control and the power consumption of the AATC system. The authors are now in the process of developing enhanced train control algorithms to supplement the safety critical controller in order to smooth out train trajectories through coordinated control of multiple trains, and to reduce energy consumption and power infrastructure requirements. The control algorithms so far considered include (1) reducing peak power consumption to avoid voltage sags, especially during an outage or while clearing a backup, (2) rapid and smooth recovery from a backup, (3) avoiding oscillations due to train interference, (4) limiting needle peaks in power demand at substations to some specified level, (5) coasting, and (6) coordinating train movement, e.g., starts/stops and hills.« less

Operational performance of the three bean salad control algorithm on the ACRR

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ball, R.M.; Madaras, J.J.; Trowbridge, F.R. Jr.

Experimental tests on the Annular Core Research Reactor have confirmed that the Three-Bean-Salad'' control algorithm based on the Pontryagin maximum principle can change the power of a nuclear reactor many decades with a very fast startup rate and minimal overshoot. The paper describes the results of simulations and operations up to 25 MW and 87 decades per minute.

Operational performance of the three bean salad control algorithm on the ACRR

NASA Astrophysics Data System (ADS)

Ball, Russell M.; Madaras, John J.; Trowbridge, F. Ray; Talley, Darren G.; Parma, Edward J.

1991-01-01

Experimental tests on the Annular Core Research Reactor have confirmed that the ``Three-Bean-Salad'' control algorithm based on the Pontryagin maximum principle can change the power of a nuclear reactor many decades with a very fast startup rate and minimal overshoot. The paper describes the results of simulations and operations up to 25 MW and 87 decades per minute.

Research on optimization of combustion efficiency of thermal power unit based on genetic algorithm

NASA Astrophysics Data System (ADS)

Zhou, Qiongyang

2018-04-01

In order to improve the economic performance and reduce pollutant emissions of thermal power units, the characteristics of neural network in establishing boiler combustion model are analyzed based on the analysis of the main factors affecting boiler efficiency by using orthogonal method. In addition, on the basis of this model, the genetic algorithm is used to find the best control amount of the furnace combustion in a certain working condition. Through the genetic algorithm based on real number encoding and roulette selection is concluded: the best control quantity at a condition of furnace combustion can be combined with the boiler combustion system model for neural network training. The precision of the neural network model is further improved, and the basic work is laid for the research of the whole boiler combustion optimization system.

Automated power management and control

NASA Technical Reports Server (NTRS)

Dolce, James L.

1991-01-01

A comprehensive automation design is being developed for Space Station Freedom's electric power system. A joint effort between NASA's Office of Aeronautics and Exploration Technology and NASA's Office of Space Station Freedom, it strives to increase station productivity by applying expert systems and conventional algorithms to automate power system operation. The initial station operation will use ground-based dispatches to perform the necessary command and control tasks. These tasks constitute planning and decision-making activities that strive to eliminate unplanned outages. We perceive an opportunity to help these dispatchers make fast and consistent on-line decisions by automating three key tasks: failure detection and diagnosis, resource scheduling, and security analysis. Expert systems will be used for the diagnostics and for the security analysis; conventional algorithms will be used for the resource scheduling.

Dynamic ADMM for Real-Time Optimal Power Flow: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Zhang, Yijian; Hong, Mingyi

This paper considers distribution networks featuring distributed energy resources (DERs), and develops a dynamic optimization method to maximize given operational objectives in real time while adhering to relevant network constraints. The design of the dynamic algorithm is based on suitable linearizations of the AC power flow equations, and it leverages the so-called alternating direction method of multipliers (ADMM). The steps of the ADMM, however, are suitably modified to accommodate appropriate measurements from the distribution network and the DERs. With the aid of these measurements, the resultant algorithm can enforce given operational constraints in spite of inaccuracies in the representation ofmore » the AC power flows, and it avoids ubiquitous metering to gather the state of non-controllable resources. Optimality and convergence of the propose algorithm are established in terms of tracking of the solution of a convex surrogate of the AC optimal power flow problem.« less

Maximum power point tracking techniques for wind energy systems using three levels boost converter

NASA Astrophysics Data System (ADS)

Tran, Cuong Hung; Nollet, Frédéric; Essounbouli, Najib; Hamzaoui, Abdelaziz

2018-05-01

This paper presents modeling and simulation of three level Boost DC-DC converter in Wind Energy Conversion System (WECS). Three-level Boost converter has significant advantage compared to conventional Boost. A maximum power point tracking (MPPT) method for a variable speed wind turbine using permanent magnet synchronous generator (PMSG) is also presented. Simulation of three-level Boost converter topology with Perturb and Observe algorithm and Fuzzy Logic Control is implemented in MATLAB/SIMULINK. Results of this simulation show that the system with MPPT using fuzzy logic controller has better performance to the Perturb and Observe algorithm: fast response under changing conditions and small oscillation.

Linear triangular optimization technique and pricing scheme in residential energy management systems

NASA Astrophysics Data System (ADS)

Anees, Amir; Hussain, Iqtadar; AlKhaldi, Ali Hussain; Aslam, Muhammad

2018-06-01

This paper presents a new linear optimization algorithm for power scheduling of electric appliances. The proposed system is applied in a smart home community, in which community controller acts as a virtual distribution company for the end consumers. We also present a pricing scheme between community controller and its residential users based on real-time pricing and likely block rates. The results of the proposed optimization algorithm demonstrate that by applying the anticipated technique, not only end users can minimise the consumption cost, but it can also reduce the power peak to an average ratio which will be beneficial for the utilities as well.

Emergence of an optimal search strategy from a simple random walk

PubMed Central

Sakiyama, Tomoko; Gunji, Yukio-Pegio

2013-01-01

In reports addressing animal foraging strategies, it has been stated that Lévy-like algorithms represent an optimal search strategy in an unknown environment, because of their super-diffusion properties and power-law-distributed step lengths. Here, starting with a simple random walk algorithm, which offers the agent a randomly determined direction at each time step with a fixed move length, we investigated how flexible exploration is achieved if an agent alters its randomly determined next step forward and the rule that controls its random movement based on its own directional moving experiences. We showed that our algorithm led to an effective food-searching performance compared with a simple random walk algorithm and exhibited super-diffusion properties, despite the uniform step lengths. Moreover, our algorithm exhibited a power-law distribution independent of uniform step lengths. PMID:23804445

Emergence of an optimal search strategy from a simple random walk.

PubMed

Sakiyama, Tomoko; Gunji, Yukio-Pegio

2013-09-06

In reports addressing animal foraging strategies, it has been stated that Lévy-like algorithms represent an optimal search strategy in an unknown environment, because of their super-diffusion properties and power-law-distributed step lengths. Here, starting with a simple random walk algorithm, which offers the agent a randomly determined direction at each time step with a fixed move length, we investigated how flexible exploration is achieved if an agent alters its randomly determined next step forward and the rule that controls its random movement based on its own directional moving experiences. We showed that our algorithm led to an effective food-searching performance compared with a simple random walk algorithm and exhibited super-diffusion properties, despite the uniform step lengths. Moreover, our algorithm exhibited a power-law distribution independent of uniform step lengths.

The Use of Software Agents for Autonomous Control of a DC Space Power System

NASA Technical Reports Server (NTRS)

May, Ryan D.; Loparo, Kenneth A.

2014-01-01

In order to enable manned deep-space missions, the spacecraft must be controlled autonomously using on-board algorithms. A control architecture is proposed to enable this autonomous operation for an spacecraft electric power system and then implemented using a highly distributed network of software agents. These agents collaborate and compete with each other in order to implement each of the control functions. A subset of this control architecture is tested against a steadystate power system simulation and found to be able to solve a constrained optimization problem with competing objectives using only local information.

Design and implementation of co-operative control strategy for hybrid AC/DC microgrids

NASA Astrophysics Data System (ADS)

Mahmud, Rasel

This thesis is mainly divided in two major sections: 1) Modeling and control of AC microgrid, DC microgrid, Hybrid AC/DC microgrid using distributed co-operative control, and 2) Development of a four bus laboratory prototype of an AC microgrid system. At first, a distributed cooperative control (DCC) for a DC microgrid considering the state-of-charge (SoC) of the batteries in a typical plug-in-electric-vehicle (PEV) is developed. In DC microgrids, this methodology is developed to assist the load sharing amongst the distributed generation units (DGs), according to their ratings with improved voltage regulation. Subsequently, a DCC based control algorithm for AC microgrid is also investigated to improve the performance of AC microgrid in terms of power sharing among the DGs, voltage regulation and frequency deviation. The results validate the advantages of the proposed methodology as compared to traditional droop control of AC microgrid. The DCC-based control methodology for AC microgrid and DC microgrid are further expanded to develop a DCC-based power management algorithm for hybrid AC/DC microgrid. The developed algorithm for hybrid microgrid controls the power flow through the interfacing converter (IC) between the AC and DC microgrids. This will facilitate the power sharing between the DGs according to their power ratings. Moreover, it enables the fixed scheduled power delivery at different operating conditions, while maintaining good voltage regulation and improved frequency profile. The second section provides a detailed explanation and step-by-step design and development of an AC/DC microgrid testbed. Controllers for the three-phase inverters are designed and tested on different generation units along with their corresponding inductor-capacitor-inductor (LCL) filters to eliminate the switching frequency harmonics. Electric power distribution line models are developed to form the microgrid network topology. Voltage and current sensors are placed in the proper positions to achieve a full visibility over the microgrid. A running average filter (RAF) based enhanced phase-locked-loop (EPLL) is designed and implemented to extract frequency and phase angle information. A PLL-based synchronizing scheme is also developed to synchronize the DGs to the microgrid. The developed laboratory prototype runs on dSpace platform for real time data acquisition, communication and controller implementation.

Automatic control of solar power plants

NASA Astrophysics Data System (ADS)

Ermakov, V. S.; Dubilovich, V. M.

1982-02-01

The automatic control of the heliostat field of a 200-MW solar power plant is discussed. The advantages of the decentralized control principle with the solution of a number of individual problems in a single control center are emphasized. The basic requirements on heliostat construction are examined, and possible functional schemes for the automatic control of a heliostat field are described. It is proposed that groups of heliostats can be controlled from a single center and on the basis of a single algorithm.

Hybrid zero-voltage switching (ZVS) control for power inverters

DOEpatents

Amirahmadi, Ahmadreza; Hu, Haibing; Batarseh, Issa

2016-11-01

A power inverter combination includes a half-bridge power inverter including first and second semiconductor power switches receiving input power having an intermediate node therebetween providing an inductor current through an inductor. A controller includes input comparison circuitry receiving the inductor current having outputs coupled to first inputs of pulse width modulation (PWM) generation circuitry, and a predictive control block having an output coupled to second inputs of the PWM generation circuitry. The predictive control block is coupled to receive a measure of Vin and an output voltage at a grid connection point. A memory stores a current control algorithm configured for resetting a PWM period for a switching signal applied to control nodes of the first and second power switch whenever the inductor current reaches a predetermined upper limit or a predetermined lower limit.

Achievement of radiative feedback control for long-pulse operation on EAST

NASA Astrophysics Data System (ADS)

Wu, K.; Yuan, Q. P.; Xiao, B. J.; Wang, L.; Duan, Y. M.; Chen, J. B.; Zheng, X. W.; Liu, X. J.; Zhang, B.; Xu, J. C.; Luo, Z. P.; Zang, Q.; Li, Y. Y.; Feng, W.; Wu, J. H.; Yang, Z. S.; Zhang, L.; Luo, G.-N.; Gong, X. Z.; Hu, L. Q.; Hu, J. S.; Li, J.

2018-05-01

The active feedback control of radiated power to prevent divertor target plates overheating during long-pulse operation has been developed and implemented on EAST. The radiation control algorithm, with impurity seeding via a supersonic molecular beam injection (SMBI) system, has shown great success in both reliability and stability. By seeding a sequence of short neon (Ne) impurity pulses with the SMBI from the outer mid-plane, the radiated power of the bulk plasma can be well controlled, and the duration of radiative control (feedforward and feedback) is 4.5 s during a discharge of 10 s. Reliable control of the total radiated power of bulk plasma has been successfully achieved in long-pulse upper single null (USN) discharges with a tungsten divertor. The achieved control range of {{f}rad} is 20%–30% in L-mode regimes and 18%–36% in H-mode regimes. The temperature of the divertor target plates was maintained at a low level during the radiative control phase. The peak particle flux on the divertor target was decreased by feedforward Ne injection in the L-mode discharges, while the Ne pulses from the SMBI had no influence on the peak particle flux because of the very small injecting volume. It is shown that although the radiated power increased, no serious reduction of plasma-stored energy or confinement was observed during the control phase. The success of the radiation control algorithm and current experiments in radiated power control represents a significant advance for steady-state divertor radiation and heat flux control on EAST for near-future long-pulse operation.

Coordinated Control of Wind Turbine and Energy Storage System for Reducing Wind Power Fluctuation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Chunghun; Muljadi, Eduard; Chung, Chung Choo

This paper proposes a method for the coordinated control of a wind turbine and an energy storage system (ESS). Because wind power (WP) is highly dependent on wind speed, which is variable, severe stability problems can be caused in power systems, especially when the WP has a high penetration level. To solve this problem, many power generation corporations or grid operators have begun using ESSs. An ESS has very quick response and good performance for reducing the impact of WP fluctuation; however, its installation cost is high. Therefore, it is important to design the control algorithm by considering both themore » ESS capacity and WP fluctuation. Thus, we propose a control algorithm to mitigate the WP fluctuation by using the coordinated control between the wind turbine and the ESS by considering the ESS capacity and the WP fluctuation. Using de-loaded control, according to the WP fluctuation and ESS capacity, we can expand the ESS lifespan and improve grid reliability by avoiding the extreme value of state of charge (SoC) (i.e., 0 or 1 pu). The effectiveness of the proposed method was validated via MATLAB/Simulink by considering a small power system that includes both a wind turbine generator and conventional generators that react to system frequency deviation. We found that the proposed method has better performance in SoC management, thereby improving the frequency regulation by mitigating the impact of the WP fluctuation on the small power system.« less

Coordinated Control of Wind Turbine and Energy Storage System for Reducing Wind Power Fluctuation

DOE PAGES

Kim, Chunghun; Muljadi, Eduard; Chung, Chung Choo

2017-12-27

This paper proposes a method for the coordinated control of a wind turbine and an energy storage system (ESS). Because wind power (WP) is highly dependent on wind speed, which is variable, severe stability problems can be caused in power systems, especially when the WP has a high penetration level. To solve this problem, many power generation corporations or grid operators have begun using ESSs. An ESS has very quick response and good performance for reducing the impact of WP fluctuation; however, its installation cost is high. Therefore, it is important to design the control algorithm by considering both themore » ESS capacity and WP fluctuation. Thus, we propose a control algorithm to mitigate the WP fluctuation by using the coordinated control between the wind turbine and the ESS by considering the ESS capacity and the WP fluctuation. Using de-loaded control, according to the WP fluctuation and ESS capacity, we can expand the ESS lifespan and improve grid reliability by avoiding the extreme value of state of charge (SoC) (i.e., 0 or 1 pu). The effectiveness of the proposed method was validated via MATLAB/Simulink by considering a small power system that includes both a wind turbine generator and conventional generators that react to system frequency deviation. We found that the proposed method has better performance in SoC management, thereby improving the frequency regulation by mitigating the impact of the WP fluctuation on the small power system.« less

Experience in connecting the power generating units of thermal power plants to automatic secondary frequency regulation within the united power system of Russia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhukov, A. V.; Komarov, A. N.; Safronov, A. N.

The principles of central control of the power generating units of thermal power plants by automatic secondary frequency and active power overcurrent regulation systems, and the algorithms for interactions between automatic power control systems for the power production units in thermal power plants and centralized systems for automatic frequency and power regulation, are discussed. The order of switching the power generating units of thermal power plants over to control by a centralized system for automatic frequency and power regulation and by the Central Coordinating System for automatic frequency and power regulation is presented. The results of full-scale system tests ofmore » the control of power generating units of the Kirishskaya, Stavropol, and Perm GRES (State Regional Electric Power Plants) by the Central Coordinating System for automatic frequency and power regulation at the United Power System of Russia on September 23-25, 2008, are reported.« less

A power autonomous monopedal robot

NASA Astrophysics Data System (ADS)

Krupp, Benjamin T.; Pratt, Jerry E.

2006-05-01

We present the design and initial results of a power-autonomous planar monopedal robot. The robot is a gasoline powered, two degree of freedom robot that runs in a circle, constrained by a boom. The robot uses hydraulic Series Elastic Actuators, force-controllable actuators which provide high force fidelity, moderate bandwidth, and low impedance. The actuators are mounted in the body of the robot, with cable drives transmitting power to the hip and knee joints of the leg. A two-stroke, gasoline engine drives a constant displacement pump which pressurizes an accumulator. Absolute position and spring deflection of each of the Series Elastic Actuators are measured using linear encoders. The spring deflection is translated into force output and compared to desired force in a closed loop force-control algorithm implemented in software. The output signal of each force controller drives high performance servo valves which control flow to each of the pistons of the actuators. In designing the robot, we used a simulation-based iterative design approach. Preliminary estimates of the robot's physical parameters were based on past experience and used to create a physically realistic simulation model of the robot. Next, a control algorithm was implemented in simulation to produce planar hopping. Using the joint power requirements and range of motions from simulation, we worked backward specifying pulley diameter, piston diameter and stroke, hydraulic pressure and flow, servo valve flow and bandwidth, gear pump flow, and engine power requirements. Components that meet or exceed these specifications were chosen and integrated into the robot design. Using CAD software, we calculated the physical parameters of the robot design, replaced the original estimates with the CAD estimates, and produced new joint power requirements. We iterated on this process, resulting in a design which was prototyped and tested. The Monopod currently runs at approximately 1.2 m/s with the weight of all the power generating components, but powered from an off-board pump. On a test stand, the eventual on-board power system generates enough pressure and flow to meet the requirements of these runs and we are currently integrating the power system into the real robot. When operated from an off-board system without carrying the weight of the power generating components, the robot currently runs at approximately 2.25 m/s. Ongoing work is focused on integrating the power system into the robot, improving the control algorithm, and investigating methods for improving efficiency.

Decentralized Feedback Controllers for Exponential Stabilization of Hybrid Periodic Orbits: Application to Robotic Walking.

PubMed

Hamed, Kaveh Akbari; Gregg, Robert D

2016-07-01

This paper presents a systematic algorithm to design time-invariant decentralized feedback controllers to exponentially stabilize periodic orbits for a class of hybrid dynamical systems arising from bipedal walking. The algorithm assumes a class of parameterized and nonlinear decentralized feedback controllers which coordinate lower-dimensional hybrid subsystems based on a common phasing variable. The exponential stabilization problem is translated into an iterative sequence of optimization problems involving bilinear and linear matrix inequalities, which can be easily solved with available software packages. A set of sufficient conditions for the convergence of the iterative algorithm to a stabilizing decentralized feedback control solution is presented. The power of the algorithm is demonstrated by designing a set of local nonlinear controllers that cooperatively produce stable walking for a 3D autonomous biped with 9 degrees of freedom, 3 degrees of underactuation, and a decentralization scheme motivated by amputee locomotion with a transpelvic prosthetic leg.

Decentralized Feedback Controllers for Exponential Stabilization of Hybrid Periodic Orbits: Application to Robotic Walking*

PubMed Central

Hamed, Kaveh Akbari; Gregg, Robert D.

2016-01-01

This paper presents a systematic algorithm to design time-invariant decentralized feedback controllers to exponentially stabilize periodic orbits for a class of hybrid dynamical systems arising from bipedal walking. The algorithm assumes a class of parameterized and nonlinear decentralized feedback controllers which coordinate lower-dimensional hybrid subsystems based on a common phasing variable. The exponential stabilization problem is translated into an iterative sequence of optimization problems involving bilinear and linear matrix inequalities, which can be easily solved with available software packages. A set of sufficient conditions for the convergence of the iterative algorithm to a stabilizing decentralized feedback control solution is presented. The power of the algorithm is demonstrated by designing a set of local nonlinear controllers that cooperatively produce stable walking for a 3D autonomous biped with 9 degrees of freedom, 3 degrees of underactuation, and a decentralization scheme motivated by amputee locomotion with a transpelvic prosthetic leg. PMID:27990059

Prognostics of Power Electronics, Methods and Validation Experiments

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.; Celaya, Jose R.; Biswas, Gautam; Goebel, Kai

2012-01-01

Abstract Failure of electronic devices is a concern for future electric aircrafts that will see an increase of electronics to drive and control safety-critical equipment throughout the aircraft. As a result, investigation of precursors to failure in electronics and prediction of remaining life of electronic components is of key importance. DC-DC power converters are power electronics systems employed typically as sourcing elements for avionics equipment. Current research efforts in prognostics for these power systems focuses on the identification of failure mechanisms and the development of accelerated aging methodologies and systems to accelerate the aging process of test devices, while continuously measuring key electrical and thermal parameters. Preliminary model-based prognostics algorithms have been developed making use of empirical degradation models and physics-inspired degradation model with focus on key components like electrolytic capacitors and power MOSFETs (metal-oxide-semiconductor-field-effect-transistor). This paper presents current results on the development of validation methods for prognostics algorithms of power electrolytic capacitors. Particularly, in the use of accelerated aging systems for algorithm validation. Validation of prognostics algorithms present difficulties in practice due to the lack of run-to-failure experiments in deployed systems. By using accelerated experiments, we circumvent this problem in order to define initial validation activities.

Intelligent energy harvesting scheme for microbial fuel cells: Maximum power point tracking and voltage overshoot avoidance

NASA Astrophysics Data System (ADS)

Alaraj, Muhannad; Radenkovic, Miloje; Park, Jae-Do

2017-02-01

Microbial fuel cells (MFCs) are renewable and sustainable energy sources that can be used for various applications. The MFC output power depends on its biochemical conditions as well as the terminal operating points in terms of output voltage and current. There exists one operating point that gives the maximum possible power from the MFC, maximum power point (MPP), for a given operating condition. However, this MPP may vary and needs to be tracked in order to maintain the maximum power extraction from the MFC. Furthermore, MFC reactors often develop voltage overshoots that cause drastic drops in the terminal voltage, current, and the output power. When the voltage overshoot happens, an additional control measure is necessary as conventional MPPT algorithms will fail because of the change in the voltage-current relationship. In this paper, the extremum seeking (ES) algorithm was used to track the varying MPP and a voltage overshoot avoidance (VOA) algorithm is developed to manage the voltage overshoot conditions. The proposed ES-MPPT with VOA algorithm was able to extract 197.2 mJ during 10-min operation avoiding voltage overshoot, while the ES MPPT-only scheme stopped harvesting after only 18.75 mJ because of the voltage overshoot happened at 0.4 min.

Computing Optic Flow with ArduEye Vision Sensor

DTIC Science & Technology

2013-01-01

processing algorithm that can be applied to the flight control of other robotic platforms. 15. SUBJECT TERMS Optical flow, ArduEye, vision based ...2 Figure 2. ArduEye vision chip on Stonyman breakout board connected to Arduino Mega (8) (left) and the Stonyman vision chips (7...robotic platforms. There is a significant need for small, light , less power-hungry sensors and sensory data processing algorithms in order to control the

Autonomous dynamic obstacle avoidance for bacteria-powered microrobots (BPMs) with modified vector field histogram

PubMed Central

Kim, Hoyeon; Cheang, U. Kei

2017-01-01

In order to broaden the use of microrobots in practical fields, autonomous control algorithms such as obstacle avoidance must be further developed. However, most previous studies of microrobots used manual motion control to navigate past tight spaces and obstacles while very few studies demonstrated the use of autonomous motion. In this paper, we demonstrated a dynamic obstacle avoidance algorithm for bacteria-powered microrobots (BPMs) using electric field in fluidic environments. A BPM consists of an artificial body, which is made of SU-8, and a high dense layer of harnessed bacteria. BPMs can be controlled using externally applied electric fields due to the electrokinetic property of bacteria. For developing dynamic obstacle avoidance for BPMs, a kinematic model of BPMs was utilized to prevent collision and a finite element model was used to characteristic the deformation of an electric field near the obstacle walls. In order to avoid fast moving obstacles, we modified our previously static obstacle avoidance approach using a modified vector field histogram (VFH) method. To validate the advanced algorithm in experiments, magnetically controlled moving obstacles were used to intercept the BPMs as the BPMs move from the initial position to final position. The algorithm was able to successfully guide the BPMs to reach their respective goal positions while avoiding the dynamic obstacles. PMID:29020016

Autonomous dynamic obstacle avoidance for bacteria-powered microrobots (BPMs) with modified vector field histogram.

PubMed

Kim, Hoyeon; Cheang, U Kei; Kim, Min Jun

2017-01-01

In order to broaden the use of microrobots in practical fields, autonomous control algorithms such as obstacle avoidance must be further developed. However, most previous studies of microrobots used manual motion control to navigate past tight spaces and obstacles while very few studies demonstrated the use of autonomous motion. In this paper, we demonstrated a dynamic obstacle avoidance algorithm for bacteria-powered microrobots (BPMs) using electric field in fluidic environments. A BPM consists of an artificial body, which is made of SU-8, and a high dense layer of harnessed bacteria. BPMs can be controlled using externally applied electric fields due to the electrokinetic property of bacteria. For developing dynamic obstacle avoidance for BPMs, a kinematic model of BPMs was utilized to prevent collision and a finite element model was used to characteristic the deformation of an electric field near the obstacle walls. In order to avoid fast moving obstacles, we modified our previously static obstacle avoidance approach using a modified vector field histogram (VFH) method. To validate the advanced algorithm in experiments, magnetically controlled moving obstacles were used to intercept the BPMs as the BPMs move from the initial position to final position. The algorithm was able to successfully guide the BPMs to reach their respective goal positions while avoiding the dynamic obstacles.

Enhancements on the Convex Programming Based Powered Descent Guidance Algorithm for Mars Landing

NASA Technical Reports Server (NTRS)

Acikmese, Behcet; Blackmore, Lars; Scharf, Daniel P.; Wolf, Aron

2008-01-01

In this paper, we present enhancements on the powered descent guidance algorithm developed for Mars pinpoint landing. The guidance algorithm solves the powered descent minimum fuel trajectory optimization problem via a direct numerical method. Our main contribution is to formulate the trajectory optimization problem, which has nonconvex control constraints, as a finite dimensional convex optimization problem, specifically as a finite dimensional second order cone programming (SOCP) problem. SOCP is a subclass of convex programming, and there are efficient SOCP solvers with deterministic convergence properties. Hence, the resulting guidance algorithm can potentially be implemented onboard a spacecraft for real-time applications. Particularly, this paper discusses the algorithmic improvements obtained by: (i) Using an efficient approach to choose the optimal time-of-flight; (ii) Using a computationally inexpensive way to detect the feasibility/ infeasibility of the problem due to the thrust-to-weight constraint; (iii) Incorporating the rotation rate of the planet into the problem formulation; (iv) Developing additional constraints on the position and velocity to guarantee no-subsurface flight between the time samples of the temporal discretization; (v) Developing a fuel-limited targeting algorithm; (vi) Initial result on developing an onboard table lookup method to obtain almost fuel optimal solutions in real-time.

Joint subchannel pairing and power control for cognitive radio networks with amplify-and-forward relaying.

PubMed

Shen, Yanyan; Wang, Shuqiang; Wei, Zhiming

2014-01-01

Dynamic spectrum sharing has drawn intensive attention in cognitive radio networks. The secondary users are allowed to use the available spectrum to transmit data if the interference to the primary users is maintained at a low level. Cooperative transmission for secondary users can reduce the transmission power and thus improve the performance further. We study the joint subchannel pairing and power allocation problem in relay-based cognitive radio networks. The objective is to maximize the sum rate of the secondary user that is helped by an amplify-and-forward relay. The individual power constraints at the source and the relay, the subchannel pairing constraints, and the interference power constraints are considered. The problem under consideration is formulated as a mixed integer programming problem. By the dual decomposition method, a joint optimal subchannel pairing and power allocation algorithm is proposed. To reduce the computational complexity, two suboptimal algorithms are developed. Simulations have been conducted to verify the performance of the proposed algorithms in terms of sum rate and average running time under different conditions.

Optimal Control of a Surge-Mode WEC in Random Waves

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chertok, Allan; Ceberio, Olivier; Staby, Bill

2016-08-30

The objective of this project was to develop one or more real-time feedback and feed-forward (MPC) control algorithms for an Oscillating Surge Wave Converter (OSWC) developed by RME called SurgeWEC™ that leverages recent innovations in wave energy converter (WEC) control theory to maximize power production in random wave environments. The control algorithms synthesized innovations in dynamic programming and nonlinear wave dynamics using anticipatory wave sensors and localized sensor measurements; e.g. position and velocity of the WEC Power Take Off (PTO), with predictive wave forecasting data. The result was an advanced control system that uses feedback or feed-forward data from anmore » array of sensor channels comprised of both localized and deployed sensors fused into a single decision process that optimally compensates for uncertainties in the system dynamics, wave forecasts, and sensor measurement errors.« less

A self-tuning automatic voltage regulator designed for an industrial environment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Flynn, D.; Hogg, B.W.; Swidenbank, E.

Examination of the performance of fixed parameter controllers has resulted in the development of self-tuning strategies for excitation control of turbogenerator systems. In conjunction with the advanced control algorithms, sophisticated measurement techniques have previously been adopted on micromachine systems to provide generator terminal quantities. In power stations, however, a minimalist hardware arrangement would be selected leading to relatively simple measurement techniques. The performance of a range of self-tuning schemes is investigated on an industrial test-bed, employing a typical industrial hardware measurement system. Individual controllers are implemented on a standard digital automatic voltage regulator, as installed in power stations. This employsmore » a VME platform, and the self-tuning algorithms are introduced by linking to a transputer network. The AVR includes all normal features, such as field forcing, VAR limiting and overflux protection. Self-tuning controller performance is compared with that of a fixed gain digital AVR.« less

Decentralized automatic generation control of interconnected power systems incorporating asynchronous tie-lines.

PubMed

Ibraheem; Hasan, Naimul; Hussein, Arkan Ahmed

2014-01-01

This Paper presents the design of decentralized automatic generation controller for an interconnected power system using PID, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The designed controllers are tested on identical two-area interconnected power systems consisting of thermal power plants. The area interconnections between two areas are considered as (i) AC tie-line only (ii) Asynchronous tie-line. The dynamic response analysis is carried out for 1% load perturbation. The performance of the intelligent controllers based on GA and PSO has been compared with the conventional PID controller. The investigations of the system dynamic responses reveal that PSO has the better dynamic response result as compared with PID and GA controller for both type of area interconnection.

Distributed topology control algorithm for multihop wireless netoworks

NASA Technical Reports Server (NTRS)

Borbash, S. A.; Jennings, E. H.

2002-01-01

We present a network initialization algorithmfor wireless networks with distributed intelligence. Each node (agent) has only local, incomplete knowledge and it must make local decisions to meet a predefined global objective. Our objective is to use power control to establish a topology based onthe relative neighborhood graph which has good overall performance in terms of power usage, low interference, and reliability.

Power system security enhancement through direct non-disruptive load control

NASA Astrophysics Data System (ADS)

Ramanathan, Badri Narayanan

The transition to a competitive market structure raises significant concerns regarding reliability of the power grid. A need to build tools for security assessment that produce operating limit boundaries for both static and dynamic contingencies is recognized. Besides, an increase in overall uncertainty in operating conditions makes corrective actions at times ineffective leaving the system vulnerable to instability. The tools that are in place for stability enhancement are mostly corrective and suffer from lack of robustness to operating condition changes. They often pose serious coordination challenges. With deregulation, there have also been ownership and responsibility issues associated with stability controls. However, the changing utility business model and the developments in enabling technologies such as two-way communication, metering, and control open up several new possibilities for power system security enhancement. This research proposes preventive modulation of selected loads through direct control for power system security enhancement. Two main contributions of this research are the following: development of an analysis framework and two conceptually different analysis approaches for load modulation to enhance oscillatory stability, and the development and study of algorithms for real-time modulation of thermostatic loads. The underlying analysis framework is based on the Structured Singular Value (SSV or mu) theory. Based on the above framework, two fundamentally different approaches towards analysis of the amount of load modulation for desired stability performance have been developed. Both the approaches have been tested on two different test systems: CIGRE Nordic test system and an equivalent of the Western Electric Coordinating Council test system. This research also develops algorithms for real-time modulation of thermostatic loads that use the results of the analysis. In line with some recent load management programs executed by utilities, two different algorithms based on dynamic programming are proposed for air-conditioner loads, while a decision-tree based algorithm is proposed for water-heater loads. An optimization framework has been developed employing the above algorithms. Monte Carlo simulations have been performed using this framework with the objective of studying the impact of different parameters and constraints on the effectiveness as well as the effect of control. The conclusions drawn from this research strongly advocate direct load control for stability enhancement from the perspectives of robustness and coordination, as well as economic viability and the developments towards availability of the institutional framework for load participation in providing system reliability services.

Attitude Control of Nanosatellites by Paddle Motion Using Elastic Hinges Actuated by Shape Memory Alloy

NASA Astrophysics Data System (ADS)

Iai, Masafumi; Durali, Mohammad; Hatsuzawa, Takeshi

Recent research has been extending the applications of small satellites called microsatellites, nanosatellites, or picosatellites. To further improve capability of those satellites, a lightweight, active attitude-control mechanism is needed. This paper proposes a concept of inertial orientation control, an attitude control method using movable solar arrays. This method is made suitable for nanosatellites by the use of shape memory alloy (SMA)-actuated elastic hinges and a simple maneuver generation algorithm. The combination of SMA and an elastic hinge allows the hinge to remain lightweight and free of frictional or rolling contacts. Changes in the shrinking and stretching speeds of the SMA were measured in a vacuum chamber. The proposed algorithm constructs a maneuver to achieve arbitrary attitude change by repeating simple maneuvers called unit maneuvers. Provided with three types of unit maneuvers, each degree of freedom of the satellite can be controlled independently. Such construction requires only simple calculations, making it a practical algorithm for a nanosatellite with limited computational capability. In addition, power generation variation caused by maneuvers was analyzed to confirm that a maneuver from any initial attitude to an attitude facing the sun was justifiable in terms of the power budget.

Improved Power System Stability Using Backtracking Search Algorithm for Coordination Design of PSS and TCSC Damping Controller.

PubMed

Niamul Islam, Naz; Hannan, M A; Mohamed, Azah; Shareef, Hussain

2016-01-01

Power system oscillation is a serious threat to the stability of multimachine power systems. The coordinated control of power system stabilizers (PSS) and thyristor-controlled series compensation (TCSC) damping controllers is a commonly used technique to provide the required damping over different modes of growing oscillations. However, their coordinated design is a complex multimodal optimization problem that is very hard to solve using traditional tuning techniques. In addition, several limitations of traditionally used techniques prevent the optimum design of coordinated controllers. In this paper, an alternate technique for robust damping over oscillation is presented using backtracking search algorithm (BSA). A 5-area 16-machine benchmark power system is considered to evaluate the design efficiency. The complete design process is conducted in a linear time-invariant (LTI) model of a power system. It includes the design formulation into a multi-objective function from the system eigenvalues. Later on, nonlinear time-domain simulations are used to compare the damping performances for different local and inter-area modes of power system oscillations. The performance of the BSA technique is compared against that of the popular particle swarm optimization (PSO) for coordinated design efficiency. Damping performances using different design techniques are compared in term of settling time and overshoot of oscillations. The results obtained verify that the BSA-based design improves the system stability significantly. The stability of the multimachine power system is improved by up to 74.47% and 79.93% for an inter-area mode and a local mode of oscillation, respectively. Thus, the proposed technique for coordinated design has great potential to improve power system stability and to maintain its secure operation.

A proposed adaptive step size perturbation and observation maximum power point tracking algorithm based on photovoltaic system modeling

NASA Astrophysics Data System (ADS)

Huang, Yu

Solar energy becomes one of the major alternative renewable energy options for its huge abundance and accessibility. Due to the intermittent nature, the high demand of Maximum Power Point Tracking (MPPT) techniques exists when a Photovoltaic (PV) system is used to extract energy from the sunlight. This thesis proposed an advanced Perturbation and Observation (P&O) algorithm aiming for relatively practical circumstances. Firstly, a practical PV system model is studied with determining the series and shunt resistances which are neglected in some research. Moreover, in this proposed algorithm, the duty ratio of a boost DC-DC converter is the object of the perturbation deploying input impedance conversion to achieve working voltage adjustment. Based on the control strategy, the adaptive duty ratio step size P&O algorithm is proposed with major modifications made for sharp insolation change as well as low insolation scenarios. Matlab/Simulink simulation for PV model, boost converter control strategy and various MPPT process is conducted step by step. The proposed adaptive P&O algorithm is validated by the simulation results and detail analysis of sharp insolation changes, low insolation condition and continuous insolation variation.

A Hybrid Maximum Power Point Tracking Method for Automobile Exhaust Thermoelectric Generator

NASA Astrophysics Data System (ADS)

Quan, Rui; Zhou, Wei; Yang, Guangyou; Quan, Shuhai

2017-05-01

To make full use of the maximum output power of automobile exhaust thermoelectric generator (AETEG) based on Bi2Te3 thermoelectric modules (TEMs), taking into account the advantages and disadvantages of existing maximum power point tracking methods, and according to the output characteristics of TEMs, a hybrid maximum power point tracking method combining perturb and observe (P&O) algorithm, quadratic interpolation and constant voltage tracking method was put forward in this paper. Firstly, it searched the maximum power point with P&O algorithms and a quadratic interpolation method, then, it forced the AETEG to work at its maximum power point with constant voltage tracking. A synchronous buck converter and controller were implemented in the electric bus of the AETEG applied in a military sports utility vehicle, and the whole system was modeled and simulated with a MATLAB/Simulink environment. Simulation results demonstrate that the maximum output power of the AETEG based on the proposed hybrid method is increased by about 3.0% and 3.7% compared with that using only the P&O algorithm and the quadratic interpolation method, respectively. The shorter tracking time is only 1.4 s, which is reduced by half compared with that of the P&O algorithm and quadratic interpolation method, respectively. The experimental results demonstrate that the tracked maximum power is approximately equal to the real value using the proposed hybrid method,and it can preferentially deal with the voltage fluctuation of the AETEG with only P&O algorithm, and resolve the issue that its working point can barely be adjusted only with constant voltage tracking when the operation conditions change.

Optimal power flow with optimal placement TCSC device on 500 kV Java-Bali electrical power system using genetic Algorithm-Taguchi method

NASA Astrophysics Data System (ADS)

Apribowo, Chico Hermanu Brillianto; Ibrahim, Muhammad Hamka; Wicaksono, F. X. Rian

2018-02-01

The growing burden of the load and the complexity of the power system has had an impact on the need for optimization of power system operation. Optimal power flow (OPF) with optimal location placement and rating of thyristor controlled series capacitor (TCSC) is an effective solution used to determine the economic cost of operating the plant and regulate the power flow in the power system. The purpose of this study is to minimize the total cost of generation by placing the location and the optimal rating of TCSC using genetic algorithm-design of experiment techniques (GA-DOE). Simulation on Java-Bali system 500 kV with the amount of TCSC used by 5 compensator, the proposed method can reduce the generation cost by 0.89% compared to OPF without using TCSC.

Operational performance of the three bean salad control algorithm on the ACRR (Annular Core Research Reactor)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ball, R.M.; Madaras, J.J.; Trowbridge, F.R. Jr.

Experimental tests on the Annular Core Research Reactor have confirmed that the Three-Bean-Salad'' control algorithm based on the Pontryagin maximum principle can change the power of a nuclear reactor many decades with a very fast startup rate and minimal overshoot. The paper describes the results of simulations and operations up to 25 MW and 87 decades per minute. 3 refs., 4 figs., 1 tab.

SPS flexible system control assessment analysis

NASA Technical Reports Server (NTRS)

Balas, M. J.

1981-01-01

Active control of the Satellite Power System (SPS0, a large mechanically flexible aerospace structure is addressed. The control algorithm is the principle component in the feedback link from sensors to actuators. An analysis of the interaction of the SPS structure and its active control system is presented.

Tracking the global maximum power point of PV arrays under partial shading conditions

NASA Astrophysics Data System (ADS)

Fennich, Meryem

This thesis presents the theoretical and simulation studies of the global maximum power point tracking (MPPT) for photovoltaic systems under partial shading. The main goal is to track the maximum power point of the photovoltaic module so that the maximum possible power can be extracted from the photovoltaic panels. When several panels are connected in series with some of them shaded partially either due to clouds or shadows from neighboring buildings, several local maxima appear in the power vs. voltage curve. A power increment based MPPT algorithm is effective in identifying the global maximum from the several local maxima. Several existing MPPT algorithms are explored and the state-of-the-art power increment method is simulated and tested for various partial shading conditions. The current-voltage and power-voltage characteristics of the PV model are studied under different partial shading conditions, along with five different cases demonstrating how the MPPT algorithm performs when shading switches from one state to another. Each case is supplemented with simulation results. The method of tracking the Global MPP is based on controlling the DC-DC converter connected to the output of the PV array. A complete system simulation including the PV array, the direct current to direct current (DC-DC) converter and the MPPT is presented and tested using MATLAB software. The simulation results show that the MPPT algorithm works very well with the buck converter, while the boost converter needs further changes and implementation.

An Adaptable Power System with Software Control Algorithm

NASA Technical Reports Server (NTRS)

Castell, Karen; Bay, Mike; Hernandez-Pellerano, Amri; Ha, Kong

1998-01-01

A low cost, flexible and modular spacecraft power system design was developed in response to a call for an architecture that could accommodate multiple missions in the small to medium load range. Three upcoming satellites will use this design, with one launch date in 1999 and two in the year 2000. The design consists of modular hardware that can be scaled up or down, without additional cost, to suit missions in the 200 to 600 Watt orbital average load range. The design will be applied to satellite orbits that are circular, polar elliptical and a libration point orbit. Mission unique adaptations are accomplished in software and firmware. In designing this advanced, adaptable power system, the major goals were reduction in weight volume and cost. This power system design represents reductions in weight of 78 percent, volume of 86 percent and cost of 65 percent from previous comparable systems. The efforts to miniaturize the electronics without sacrificing performance has created streamlined power electronics with control functions residing in the system microprocessor. The power system design can handle any battery size up to 50 Amp-hour and any battery technology. The three current implementations will use both nickel cadmium and nickel hydrogen batteries ranging in size from 21 to 50 Amp-hours. Multiple batteries can be used by adding another battery module. Any solar cell technology can be used and various array layouts can be incorporated with no change in Power System Electronics (PSE) hardware. Other features of the design are the standardized interfaces between cards and subsystems and immunity to radiation effects up to 30 krad Total Ionizing Dose (TID) and 35 Mev/cm(exp 2)-kg for Single Event Effects (SEE). The control algorithm for the power system resides in a radiation-hardened microprocessor. A table driven software design allows for flexibility in mission specific requirements. By storing critical power system constants in memory, modifying the system code for other programs is simple. These constants can be altered also by ground command, or in response to an anomolous event. All critical power system functions have backup hardware functions to prevent a software or computer glitch from propagating. A number of battery charge control schemes can be implemented by selecting the proper control terms in the code. The architecture allows the design engineer to tune the system response to various system components and anticipated load profiles without costly alterations. A design trade was made with the size, weight and power dissipation of the electronics versus the performance of the power bus to load variations. Linear, fine control is maintained with a streamlined electronics design. This paper describes the hardware design as well as the software control algorithm. The challenges of closing the system control loop digitally is discussed. Control loop margin and power system performance is presented. Lab measurements are shown and compared to the system response of a hardware model running actual flight software.

Decentralized Feedback Controllers for Robust Stabilization of Periodic Orbits of Hybrid Systems: Application to Bipedal Walking.

PubMed

Hamed, Kaveh Akbari; Gregg, Robert D

2017-07-01

This paper presents a systematic algorithm to design time-invariant decentralized feedback controllers to exponentially and robustly stabilize periodic orbits for hybrid dynamical systems against possible uncertainties in discrete-time phases. The algorithm assumes a family of parameterized and decentralized nonlinear controllers to coordinate interconnected hybrid subsystems based on a common phasing variable. The exponential and [Formula: see text] robust stabilization problems of periodic orbits are translated into an iterative sequence of optimization problems involving bilinear and linear matrix inequalities. By investigating the properties of the Poincaré map, some sufficient conditions for the convergence of the iterative algorithm are presented. The power of the algorithm is finally demonstrated through designing a set of robust stabilizing local nonlinear controllers for walking of an underactuated 3D autonomous bipedal robot with 9 degrees of freedom, impact model uncertainties, and a decentralization scheme motivated by amputee locomotion with a transpelvic prosthetic leg.

Decentralized Feedback Controllers for Robust Stabilization of Periodic Orbits of Hybrid Systems: Application to Bipedal Walking

PubMed Central

Hamed, Kaveh Akbari; Gregg, Robert D.

2016-01-01

This paper presents a systematic algorithm to design time-invariant decentralized feedback controllers to exponentially and robustly stabilize periodic orbits for hybrid dynamical systems against possible uncertainties in discrete-time phases. The algorithm assumes a family of parameterized and decentralized nonlinear controllers to coordinate interconnected hybrid subsystems based on a common phasing variable. The exponential and H2 robust stabilization problems of periodic orbits are translated into an iterative sequence of optimization problems involving bilinear and linear matrix inequalities. By investigating the properties of the Poincaré map, some sufficient conditions for the convergence of the iterative algorithm are presented. The power of the algorithm is finally demonstrated through designing a set of robust stabilizing local nonlinear controllers for walking of an underactuated 3D autonomous bipedal robot with 9 degrees of freedom, impact model uncertainties, and a decentralization scheme motivated by amputee locomotion with a transpelvic prosthetic leg. PMID:28959117

Defining and Enabling Resiliency of Electric Distribution Systems With Multiple Microgrids

DOE PAGES

Chanda, Sayonsom; Srivastava, Anurag K.

2016-05-02

This paper presents a method for quantifying and enabling the resiliency of a power distribution system (PDS) using analytical hierarchical process and percolation theory. Using this metric, quantitative analysis can be done to analyze the impact of possible control decisions to pro-actively enable the resilient operation of distribution system with multiple microgrids and other resources. Developed resiliency metric can also be used in short term distribution system planning. The benefits of being able to quantify resiliency can help distribution system planning engineers and operators to justify control actions, compare different reconfiguration algorithms, develop proactive control actions to avert power systemmore » outage due to impending catastrophic weather situations or other adverse events. Validation of the proposed method is done using modified CERTS microgrids and a modified industrial distribution system. Furthermore, simulation results show topological and composite metric considering power system characteristics to quantify the resiliency of a distribution system with the proposed methodology, and improvements in resiliency using two-stage reconfiguration algorithm and multiple microgrids.« less

Development of Ada language control software for the NASA power management and distribution test bed

NASA Technical Reports Server (NTRS)

Wright, Ted; Mackin, Michael; Gantose, Dave

1989-01-01

The Ada language software developed to control the NASA Lewis Research Center's Power Management and Distribution testbed is described. The testbed is a reduced-scale prototype of the electric power system to be used on space station Freedom. It is designed to develop and test hardware and software for a 20-kHz power distribution system. The distributed, multiprocessor, testbed control system has an easy-to-use operator interface with an understandable English-text format. A simple interface for algorithm writers that uses the same commands as the operator interface is provided, encouraging interactive exploration of the system.

Investigating power capping toward energy-efficient scientific applications: Investigating Power Capping toward Energy-Efficient Scientific Applications

DOE PAGES

Haidar, Azzam; Jagode, Heike; Vaccaro, Phil; ...

2018-03-22

The emergence of power efficiency as a primary constraint in processor and system design poses new challenges concerning power and energy awareness for numerical libraries and scientific applications. Power consumption also plays a major role in the design of data centers, which may house petascale or exascale-level computing systems. At these extreme scales, understanding and improving the energy efficiency of numerical libraries and their related applications becomes a crucial part of the successful implementation and operation of the computing system. In this paper, we study and investigate the practice of controlling a compute system's power usage, and we explore howmore » different power caps affect the performance of numerical algorithms with different computational intensities. Further, we determine the impact, in terms of performance and energy usage, that these caps have on a system running scientific applications. This analysis will enable us to characterize the types of algorithms that benefit most from these power management schemes. Our experiments are performed using a set of representative kernels and several popular scientific benchmarks. Lastly, we quantify a number of power and performance measurements and draw observations and conclusions that can be viewed as a roadmap to achieving energy efficiency in the design and execution of scientific algorithms.« less

Investigating power capping toward energy-efficient scientific applications: Investigating Power Capping toward Energy-Efficient Scientific Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haidar, Azzam; Jagode, Heike; Vaccaro, Phil

The emergence of power efficiency as a primary constraint in processor and system design poses new challenges concerning power and energy awareness for numerical libraries and scientific applications. Power consumption also plays a major role in the design of data centers, which may house petascale or exascale-level computing systems. At these extreme scales, understanding and improving the energy efficiency of numerical libraries and their related applications becomes a crucial part of the successful implementation and operation of the computing system. In this paper, we study and investigate the practice of controlling a compute system's power usage, and we explore howmore » different power caps affect the performance of numerical algorithms with different computational intensities. Further, we determine the impact, in terms of performance and energy usage, that these caps have on a system running scientific applications. This analysis will enable us to characterize the types of algorithms that benefit most from these power management schemes. Our experiments are performed using a set of representative kernels and several popular scientific benchmarks. Lastly, we quantify a number of power and performance measurements and draw observations and conclusions that can be viewed as a roadmap to achieving energy efficiency in the design and execution of scientific algorithms.« less

PSO Based PI Controller Design for a Solar Charger System

PubMed Central

Yau, Her-Terng; Lin, Chih-Jer; Liang, Qin-Cheng

2013-01-01

Due to global energy crisis and severe environmental pollution, the photovoltaic (PV) system has become one of the most important renewable energy sources. Many previous studies on solar charger integrated system only focus on load charge control or switching Maximum Power Point Tracking (MPPT) and charge control modes. This study used two-stage system, which allows the overall portable solar energy charging system to implement MPPT and optimal charge control of Li-ion battery simultaneously. First, this study designs a DC/DC boost converter of solar power generation, which uses variable step size incremental conductance method (VSINC) to enable the solar cell to track the maximum power point at any time. The voltage was exported from the DC/DC boost converter to the DC/DC buck converter, so that the voltage dropped to proper voltage for charging the battery. The charging system uses constant current/constant voltage (CC/CV) method to charge the lithium battery. In order to obtain the optimum PI charge controller parameters, this study used intelligent algorithm to determine the optimum parameters. According to the simulation and experimental results, the control parameters resulted from PSO have better performance than genetic algorithms (GAs). PMID:23766713

PSO based PI controller design for a solar charger system.

PubMed

Yau, Her-Terng; Lin, Chih-Jer; Liang, Qin-Cheng

2013-01-01

Due to global energy crisis and severe environmental pollution, the photovoltaic (PV) system has become one of the most important renewable energy sources. Many previous studies on solar charger integrated system only focus on load charge control or switching Maximum Power Point Tracking (MPPT) and charge control modes. This study used two-stage system, which allows the overall portable solar energy charging system to implement MPPT and optimal charge control of Li-ion battery simultaneously. First, this study designs a DC/DC boost converter of solar power generation, which uses variable step size incremental conductance method (VSINC) to enable the solar cell to track the maximum power point at any time. The voltage was exported from the DC/DC boost converter to the DC/DC buck converter, so that the voltage dropped to proper voltage for charging the battery. The charging system uses constant current/constant voltage (CC/CV) method to charge the lithium battery. In order to obtain the optimum PI charge controller parameters, this study used intelligent algorithm to determine the optimum parameters. According to the simulation and experimental results, the control parameters resulted from PSO have better performance than genetic algorithms (GAs).

Power Allocation and Outage Probability Analysis for SDN-based Radio Access Networks

NASA Astrophysics Data System (ADS)

Zhao, Yongxu; Chen, Yueyun; Mai, Zhiyuan

2018-01-01

In this paper, performance of Access network Architecture based SDN (Software Defined Network) is analyzed with respect to the power allocation issue. A power allocation scheme PSO-PA (Particle Swarm Optimization-power allocation) algorithm is proposed, the proposed scheme is subjected to constant total power with the objective of minimizing system outage probability. The entire access network resource configuration is controlled by the SDN controller, then it sends the optimized power distribution factor to the base station source node (SN) and the relay node (RN). Simulation results show that the proposed scheme reduces the system outage probability at a low complexity.

Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system.

PubMed

Hamilton, Lei; McConley, Marc; Angermueller, Kai; Goldberg, David; Corba, Massimiliano; Kim, Louis; Moran, James; Parks, Philip D; Sang Chin; Widge, Alik S; Dougherty, Darin D; Eskandar, Emad N

2015-08-01

A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed to define brain network connectivity and neural network dynamics that vary at the individual patient level and vary over time.

Distributed finite-time containment control for double-integrator multiagent systems.

PubMed

Wang, Xiangyu; Li, Shihua; Shi, Peng

2014-09-01

In this paper, the distributed finite-time containment control problem for double-integrator multiagent systems with multiple leaders and external disturbances is discussed. In the presence of multiple dynamic leaders, by utilizing the homogeneous control technique, a distributed finite-time observer is developed for the followers to estimate the weighted average of the leaders' velocities at first. Then, based on the estimates and the generalized adding a power integrator approach, distributed finite-time containment control algorithms are designed to guarantee that the states of the followers converge to the dynamic convex hull spanned by those of the leaders in finite time. Moreover, as a special case of multiple dynamic leaders with zero velocities, the proposed containment control algorithms also work for the case of multiple stationary leaders without using the distributed observer. Simulations demonstrate the effectiveness of the proposed control algorithms.

Automated Cryocooler Monitor and Control System Software

NASA Technical Reports Server (NTRS)

Britchcliffe, Michael J.; Conroy, Bruce L.; Anderson, Paul E.; Wilson, Ahmad

2011-01-01

This software is used in an automated cryogenic control system developed to monitor and control the operation of small-scale cryocoolers. The system was designed to automate the cryogenically cooled low-noise amplifier system described in "Automated Cryocooler Monitor and Control System" (NPO-47246), NASA Tech Briefs, Vol. 35, No. 5 (May 2011), page 7a. The software contains algorithms necessary to convert non-linear output voltages from the cryogenic diode-type thermometers and vacuum pressure and helium pressure sensors, to temperature and pressure units. The control function algorithms use the monitor data to control the cooler power, vacuum solenoid, vacuum pump, and electrical warm-up heaters. The control algorithms are based on a rule-based system that activates the required device based on the operating mode. The external interface is Web-based. It acts as a Web server, providing pages for monitor, control, and configuration. No client software from the external user is required.

A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels

PubMed Central

Xia, Xu; Chen, Zhigang; Liu, Hui; Wang, Huihui; Zeng, Feng

2016-01-01

Traditional underground coalmine monitoring systems are mainly based on the use of wired transmission. However, when cables are damaged during an accident, it is difficult to obtain relevant data on environmental parameters and the emergency situation underground. To address this problem, the use of wireless sensor networks (WSNs) has been proposed. However, the shape of coalmine tunnels is not conducive to the deployment of WSNs as they are long and narrow. Therefore, issues with the network arise, such as extremely large energy consumption, very weak connectivity, long time delays, and a short lifetime. To solve these problems, in this study, a new routing protocol algorithm for multisink WSNs based on transmission power control is proposed. First, a transmission power control algorithm is used to negotiate the optimal communication radius and transmission power of each sink. Second, the non-uniform clustering idea is adopted to optimize the cluster head selection. Simulation results are subsequently compared to the Centroid of the Nodes in a Partition (CNP) strategy and show that the new algorithm delivers a good performance: power efficiency is increased by approximately 70%, connectivity is increased by approximately 15%, the cluster interference is diminished by approximately 50%, the network lifetime is increased by approximately 6%, and the delay is reduced with an increase in the number of sinks. PMID:27916917

SCADA-based Operator Support System for Power Plant Equipment Fault Forecasting

NASA Astrophysics Data System (ADS)

Mayadevi, N.; Ushakumari, S. S.; Vinodchandra, S. S.

2014-12-01

Power plant equipment must be monitored closely to prevent failures from disrupting plant availability. Online monitoring technology integrated with hybrid forecasting techniques can be used to prevent plant equipment faults. A self learning rule-based expert system is proposed in this paper for fault forecasting in power plants controlled by supervisory control and data acquisition (SCADA) system. Self-learning utilizes associative data mining algorithms on the SCADA history database to form new rules that can dynamically update the knowledge base of the rule-based expert system. In this study, a number of popular associative learning algorithms are considered for rule formation. Data mining results show that the Tertius algorithm is best suited for developing a learning engine for power plants. For real-time monitoring of the plant condition, graphical models are constructed by K-means clustering. To build a time-series forecasting model, a multi layer preceptron (MLP) is used. Once created, the models are updated in the model library to provide an adaptive environment for the proposed system. Graphical user interface (GUI) illustrates the variation of all sensor values affecting a particular alarm/fault, as well as the step-by-step procedure for avoiding critical situations and consequent plant shutdown. The forecasting performance is evaluated by computing the mean absolute error and root mean square error of the predictions.

A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels.

PubMed

Xia, Xu; Chen, Zhigang; Liu, Hui; Wang, Huihui; Zeng, Feng

2016-11-30

Traditional underground coalmine monitoring systems are mainly based on the use of wired transmission. However, when cables are damaged during an accident, it is difficult to obtain relevant data on environmental parameters and the emergency situation underground. To address this problem, the use of wireless sensor networks (WSNs) has been proposed. However, the shape of coalmine tunnels is not conducive to the deployment of WSNs as they are long and narrow. Therefore, issues with the network arise, such as extremely large energy consumption, very weak connectivity, long time delays, and a short lifetime. To solve these problems, in this study, a new routing protocol algorithm for multisink WSNs based on transmission power control is proposed. First, a transmission power control algorithm is used to negotiate the optimal communication radius and transmission power of each sink. Second, the non-uniform clustering idea is adopted to optimize the cluster head selection. Simulation results are subsequently compared to the Centroid of the Nodes in a Partition (CNP) strategy and show that the new algorithm delivers a good performance: power efficiency is increased by approximately 70%, connectivity is increased by approximately 15%, the cluster interference is diminished by approximately 50%, the network lifetime is increased by approximately 6%, and the delay is reduced with an increase in the number of sinks.

Energy-efficient orthogonal frequency division multiplexing-based passive optical network based on adaptive sleep-mode control and dynamic bandwidth allocation

NASA Astrophysics Data System (ADS)

Zhang, Chongfu; Xiao, Nengwu; Chen, Chen; Yuan, Weicheng; Qiu, Kun

2016-02-01

We propose an energy-efficient orthogonal frequency division multiplexing-based passive optical network (OFDM-PON) using adaptive sleep-mode control and dynamic bandwidth allocation. In this scheme, a bidirectional-centralized algorithm named the receiver and transmitter accurate sleep control and dynamic bandwidth allocation (RTASC-DBA), which has an overall bandwidth scheduling policy, is employed to enhance the energy efficiency of the OFDM-PON. The RTASC-DBA algorithm is used in an optical line terminal (OLT) to control the sleep mode of an optical network unit (ONU) sleep and guarantee the quality of service of different services of the OFDM-PON. The obtained results show that, by using the proposed scheme, the average power consumption of the ONU is reduced by ˜40% when the normalized ONU load is less than 80%, compared with the average power consumption without using the proposed scheme.

A Real Time Controller For Applications In Smart Structures

NASA Astrophysics Data System (ADS)

Ahrens, Christian P.; Claus, Richard O.

1990-02-01

Research in smart structures, especially the area of vibration suppression, has warranted the investigation of advanced computing environments. Real time PC computing power has limited development of high order control algorithms. This paper presents a simple Real Time Embedded Control System (RTECS) in an application of Intelligent Structure Monitoring by way of modal domain sensing for vibration control. It is compared to a PC AT based system for overall functionality and speed. The system employs a novel Reduced Instruction Set Computer (RISC) microcontroller capable of 15 million instructions per second (MIPS) continuous performance and burst rates of 40 MIPS. Advanced Complimentary Metal Oxide Semiconductor (CMOS) circuits are integrated on a single 100 mm by 160 mm printed circuit board requiring only 1 Watt of power. An operating system written in Forth provides high speed operation and short development cycles. The system allows for implementation of Input/Output (I/O) intensive algorithms and provides capability for advanced system development.

Dynamic Harmony Search with Polynomial Mutation Algorithm for Valve-Point Economic Load Dispatch

PubMed Central

Karthikeyan, M.; Sree Ranga Raja, T.

2015-01-01

Economic load dispatch (ELD) problem is an important issue in the operation and control of modern control system. The ELD problem is complex and nonlinear with equality and inequality constraints which makes it hard to be efficiently solved. This paper presents a new modification of harmony search (HS) algorithm named as dynamic harmony search with polynomial mutation (DHSPM) algorithm to solve ORPD problem. In DHSPM algorithm the key parameters of HS algorithm like harmony memory considering rate (HMCR) and pitch adjusting rate (PAR) are changed dynamically and there is no need to predefine these parameters. Additionally polynomial mutation is inserted in the updating step of HS algorithm to favor exploration and exploitation of the search space. The DHSPM algorithm is tested with three power system cases consisting of 3, 13, and 40 thermal units. The computational results show that the DHSPM algorithm is more effective in finding better solutions than other computational intelligence based methods. PMID:26491710

Dynamic Harmony Search with Polynomial Mutation Algorithm for Valve-Point Economic Load Dispatch.

PubMed

Karthikeyan, M; Raja, T Sree Ranga

2015-01-01

Economic load dispatch (ELD) problem is an important issue in the operation and control of modern control system. The ELD problem is complex and nonlinear with equality and inequality constraints which makes it hard to be efficiently solved. This paper presents a new modification of harmony search (HS) algorithm named as dynamic harmony search with polynomial mutation (DHSPM) algorithm to solve ORPD problem. In DHSPM algorithm the key parameters of HS algorithm like harmony memory considering rate (HMCR) and pitch adjusting rate (PAR) are changed dynamically and there is no need to predefine these parameters. Additionally polynomial mutation is inserted in the updating step of HS algorithm to favor exploration and exploitation of the search space. The DHSPM algorithm is tested with three power system cases consisting of 3, 13, and 40 thermal units. The computational results show that the DHSPM algorithm is more effective in finding better solutions than other computational intelligence based methods.

Scheduled power tracking control of the wind-storage hybrid system based on the reinforcement learning theory

NASA Astrophysics Data System (ADS)

Li, Ze

2017-09-01

In allusion to the intermittency and uncertainty of the wind electricity, energy storage and wind generator are combined into a hybrid system to improve the controllability of the output power. A scheduled power tracking control method is proposed based on the reinforcement learning theory and Q-learning algorithm. In this method, the state space of the environment is formed with two key factors, i.e. the state of charge of the energy storage and the difference value between the actual wind power and scheduled power, the feasible action is the output power of the energy storage, and the corresponding immediate rewarding function is designed to reflect the rationality of the control action. By interacting with the environment and learning from the immediate reward, the optimal control strategy is gradually formed. After that, it could be applied to the scheduled power tracking control of the hybrid system. Finally, the rationality and validity of the method are verified through simulation examples.

High Frequency Adaptive Instability Suppression Controls in a Liquid-Fueled Combustor

NASA Technical Reports Server (NTRS)

Kopasakis, George

2003-01-01

This effort extends into high frequency (>500 Hz), an earlier developed adaptive control algorithm for the suppression of thermo-acoustic instabilities in a liquidfueled combustor. The earlier work covered the development of a controls algorithm for the suppression of a low frequency (280 Hz) combustion instability based on simulations, with no hardware testing involved. The work described here includes changes to the simulation and controller design necessary to control the high frequency instability, augmentations to the control algorithm to improve its performance, and finally hardware testing and results with an experimental combustor rig developed for the high frequency case. The Adaptive Sliding Phasor Averaged Control (ASPAC) algorithm modulates the fuel flow in the combustor with a control phase that continuously slides back and forth within the phase region that reduces the amplitude of the instability. The results demonstrate the power of the method - that it can identify and suppress the instability even when the instability amplitude is buried in the noise of the combustor pressure. The successful testing of the ASPAC approach helped complete an important NASA milestone to demonstrate advanced technologies for low-emission combustors.

Adaptive process control using fuzzy logic and genetic algorithms

NASA Technical Reports Server (NTRS)

Karr, C. L.

1993-01-01

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

Adaptive Process Control with Fuzzy Logic and Genetic Algorithms

NASA Technical Reports Server (NTRS)

Karr, C. L.

1993-01-01

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

Genetic algorithms in adaptive fuzzy control

NASA Technical Reports Server (NTRS)

Karr, C. Lucas; Harper, Tony R.

1992-01-01

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.

Faster than Real-Time Dynamic Simulation for Large-Size Power System with Detailed Dynamic Models using High-Performance Computing Platform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Renke; Jin, Shuangshuang; Chen, Yousu

This paper presents a faster-than-real-time dynamic simulation software package that is designed for large-size power system dynamic simulation. It was developed on the GridPACKTM high-performance computing (HPC) framework. The key features of the developed software package include (1) faster-than-real-time dynamic simulation for a WECC system (17,000 buses) with different types of detailed generator, controller, and relay dynamic models, (2) a decoupled parallel dynamic simulation algorithm with optimized computation architecture to better leverage HPC resources and technologies, (3) options for HPC-based linear and iterative solvers, (4) hidden HPC details, such as data communication and distribution, to enable development centered on mathematicalmore » models and algorithms rather than on computational details for power system researchers, and (5) easy integration of new dynamic models and related algorithms into the software package.« less

Controllers for Battery Chargers and Battery Chargers Therefrom

NASA Technical Reports Server (NTRS)

Elmes, John (Inventor); Kersten, Rene (Inventor); Pepper, Michael (Inventor)

2014-01-01

A controller for a battery charger that includes a power converter has parametric sensors for providing a sensed Vin signal, a sensed Vout signal and a sensed Iout signal. A battery current regulator (BCR) is coupled to receive the sensed Iout signal and an Iout reference, and outputs a first duty cycle control signal. An input voltage regulator (IVR) receives the sensed Vin signal and a Vin reference. The IVR provides a second duty cycle control signal. A processor receives the sensed Iout signal and utilizes a Maximum Power Point Tracking (MPPT) algorithm, and provides the Vin reference to the IVR. A selection block forwards one of the first and second duty cycle control signals as a duty cycle control signal to the power converter. Dynamic switching between the first and second duty cycle control signals maximizes the power delivered to the battery.

Tunnel Ventilation Control Using Reinforcement Learning Methodology

NASA Astrophysics Data System (ADS)

Chu, Baeksuk; Kim, Dongnam; Hong, Daehie; Park, Jooyoung; Chung, Jin Taek; Kim, Tae-Hyung

The main purpose of tunnel ventilation system is to maintain CO pollutant concentration and VI (visibility index) under an adequate level to provide drivers with comfortable and safe driving environment. Moreover, it is necessary to minimize power consumption used to operate ventilation system. To achieve the objectives, the control algorithm used in this research is reinforcement learning (RL) method. RL is a goal-directed learning of a mapping from situations to actions without relying on exemplary supervision or complete models of the environment. The goal of RL is to maximize a reward which is an evaluative feedback from the environment. In the process of constructing the reward of the tunnel ventilation system, two objectives listed above are included, that is, maintaining an adequate level of pollutants and minimizing power consumption. RL algorithm based on actor-critic architecture and gradient-following algorithm is adopted to the tunnel ventilation system. The simulations results performed with real data collected from existing tunnel ventilation system and real experimental verification are provided in this paper. It is confirmed that with the suggested controller, the pollutant level inside the tunnel was well maintained under allowable limit and the performance of energy consumption was improved compared to conventional control scheme.

Evaluation of several state-of-charge algorithms

NASA Astrophysics Data System (ADS)

Espinosa, J. M.; Martin, M. E.; Burke, A. F.

1988-09-01

One of the important needs in marketing an electric vehicle is a device which reliably indicates battery state-of-charge for all types of driving. The purpose of the state-of-charge indicator is analogous to a gas gauge in an internal combustion engine powered vehicle. Many different approaches have been tried to accurately predict battery state-of-charge. This report evaluates several of these approaches. Four different algorithms were implemented into software on an IBM PC and tested using a battery test database for ALCO 2200 lead-acid batteries generated at the INEL. The database was obtained under controlled conditions which compare with the battery response in real EV use. Each algorithm is described in detail as to theory and operational functionality. Also discussed is the hardware and data requirements particular to implementing the individual algorithms. The algorithms were evaluated for accuracy using constant power, stepped power, and simulated vehicle (SFUDS79) discharge profiles. Attempts were made to explain the cause of differences between the predicted and actual state-of-charge and to provide possible remedies to correct them. Recommendations for future work on battery state-of-charge indicators are presented that utilize the hardware and software now in place in the INEL Battery Laboratory.

Photovoltaic pumping system - Comparative study analysis between direct and indirect coupling mode

NASA Astrophysics Data System (ADS)

Harrag, Abdelghani; Titraoui, Abdessalem; Bahri, Hamza; Messalti, Sabir

2017-02-01

In this paper, P&O algorithm is used in order to improve the performance of photovoltaic water pumping system in both dynamic and static response. The efficiency of the proposed algorithm has been studied successfully using a DC motor-pump powered using controller by thirty six PV modules via DC-DC boost converter derived by a P&O MPPT algorithm. Comparative study results between the direct and indirect modes coupling confirm that the proposed algorithm can effectively improve simultaneously: accuracy, rapidity, ripple and overshoot.

Twin rotor damper for the damping of stochastically forced vibrations using a power-efficient control algorithm

NASA Astrophysics Data System (ADS)

Bäumer, Richard; Terrill, Richard; Wollnack, Simon; Werner, Herbert; Starossek, Uwe

2018-01-01

The twin rotor damper (TRD), an active mass damper, uses the centrifugal forces of two eccentrically rotating control masses. In the continuous rotation mode, the preferred mode of operation, the two eccentric control masses rotate with a constant angular velocity about two parallel axes, creating, under further operational constraints, a harmonic control force in a single direction. In previous theoretical work, it was shown that this mode of operation is effective for the damping of large, harmonic vibrations of a single degree of freedom (SDOF) oscillator. In this paper, the SDOF oscillator is assumed to be affected by a stochastic excitation force and consequently responds with several frequencies. Therefore, the TRD must deviate from the continuous rotation mode to ensure the anti-phasing between the harmonic control force of the TRD and the velocity of the SDOF oscillator. It is found that the required deviation from the continuous rotation mode increases with lower vibration amplitude. Therefore, an operation of the TRD in the continuous rotation mode is no longer efficient below a specific vibration-amplitude threshold. To additionally dampen vibrations below this threshold, the TRD can switch to another, more energy-consuming mode of operation, the swinging mode in which both control masses oscillate about certain angular positions. A power-efficient control algorithm is presented which uses the continuous rotation mode for large vibrations and the swinging mode for small vibrations. To validate the control algorithm, numerical and experimental investigations are performed for a single degree of freedom oscillator under stochastic excitation. Using both modes of operation, it is shown that the control algorithm is effective for the cases of free and stochastically forced vibrations of arbitrary amplitude.

Maximum power point tracking algorithm based on sliding mode and fuzzy logic for photovoltaic sources under variable environmental conditions

NASA Astrophysics Data System (ADS)

Atik, L.; Petit, P.; Sawicki, J. P.; Ternifi, Z. T.; Bachir, G.; Della, M.; Aillerie, M.

2017-02-01

Solar panels have a nonlinear voltage-current characteristic, with a distinct maximum power point (MPP), which depends on the environmental factors, such as temperature and irradiation. In order to continuously harvest maximum power from the solar panels, they have to operate at their MPP despite the inevitable changes in the environment. Various methods for maximum power point tracking (MPPT) were developed and finally implemented in solar power electronic controllers to increase the efficiency in the electricity production originate from renewables. In this paper we compare using Matlab tools Simulink, two different MPP tracking methods, which are, fuzzy logic control (FL) and sliding mode control (SMC), considering their efficiency in solar energy production.

Digital control for the condensate system in a combined cycle power plant

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanchez Parra, M.; Fuentes Gutierrez, J.E.; Castelo Cuevas, L.

1994-12-31

This paper presents the highlights by means of which development, installation and start up of the digital control system (DCS)for the condenser and hotwell (condensate system) were performed. This system belongs to the distributed control system installed by the Instituto de Investigaciones Electricas (IIE) at the Combined Cycle Power Plant in Gomez Palacio (GP), Durango, Mexico, during the February-March period, in 1993. The main steps for development of the condenser and hotwell control system include: process modeling, definition of control strategies, algorithms, design and software development, PC simulation tests, laboratory tests with an equipment similar to the one installed atmore » the GP Power Plant, installation, and finally, start up, which was a joint effort with the GP Power Plant engineering staff.« less

Smart Energy Management of Multiple Full Cell Powered Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

MOhammad S. Alam

2007-04-23

In this research project the University of South Alabama research team has been investigating smart energy management and control of multiple fuel cell power sources when subjected to varying demands of electrical and thermal loads together with demands of hydrogen production. This research has focused on finding the optimal schedule of the multiple fuel cell power plants in terms of electric, thermal and hydrogen energy. The optimal schedule is expected to yield the lowest operating cost. Our team is also investigating the possibility of generating hydrogen using photoelectrochemical (PEC) solar cells through finding materials for efficient light harvesting photoanodes. Themore » goal is to develop an efficient and cost effective PEC solar cell system for direct electrolysis of water. In addition, models for hydrogen production, purification, and storage will be developed. The results obtained and the data collected will be then used to develop a smart energy management algorithm whose function is to maximize energy conservation within a managed set of appliances, thereby lowering O/M costs of the Fuel Cell power plant (FCPP), and allowing more hydrogen generation opportunities. The Smart Energy Management and Control (SEMaC) software, developed earlier, controls electrical loads in an individual home to achieve load management objectives such that the total power consumption of a typical residential home remains below the available power generated from a fuel cell. In this project, the research team will leverage the SEMaC algorithm developed earlier to create a neighborhood level control system.« less

Interface Control Document for the EMPACT Module that Estimates Electric Power Transmission System Response to EMP-Caused Damage

DOE Office of Scientific and Technical Information (OSTI.GOV)

Werley, Kenneth Alan; Mccown, Andrew William

The EPREP code is designed to evaluate the effects of an Electro-Magnetic Pulse (EMP) on the electric power transmission system. The EPREP code embodies an umbrella framework that allows a user to set up analysis conditions and to examine analysis results. The code links to three major physics/engineering modules. The first module describes the EM wave in space and time. The second module evaluates the damage caused by the wave on specific electric power (EP) transmission system components. The third module evaluates the consequence of the damaged network on its (reduced) ability to provide electric power to meet demand. Thismore » third module is the focus of the present paper. The EMPACT code serves as the third module. The EMPACT name denotes EMP effects on Alternating Current Transmission systems. The EMPACT algorithms compute electric power transmission network flow solutions under severely damaged network conditions. Initial solutions are often characterized by unacceptible network conditions including line overloads and bad voltages. The EMPACT code contains algorithms to adjust optimally network parameters to eliminate network problems while minimizing outages. System adjustments include automatically adjusting control equipment (generator V control, variable transformers, and variable shunts), as well as non-automatic control of generator power settings and minimal load shedding. The goal is to evaluate the minimal loss of customer load under equilibrium (steady-state) conditions during peak demand.« less

Modeling and control of distributed energy systems during transition between grid connected and standalone modes

NASA Astrophysics Data System (ADS)

Arafat, Md Nayeem

Distributed generation systems (DGs) have been penetrating into our energy networks with the advancement in the renewable energy sources and energy storage elements. These systems can operate in synchronism with the utility grid referred to as the grid connected (GC) mode of operation, or work independently, referred to as the standalone (SA) mode of operation. There is a need to ensure continuous power flow during transition between GC and SA modes, referred to as the transition mode, in operating DGs. In this dissertation, efficient and effective transition control algorithms are developed for DGs operating either independently or collectively with other units. Three techniques are proposed in this dissertation to manage the proper transition operations. In the first technique, a new control algorithm is proposed for an independent DG which can operate in SA and GC modes. The proposed transition control algorithm ensures low total harmonic distortion (THD) and less voltage fluctuation during mode transitions compared to the other techniques. In the second technique, a transition control is suggested for a collective of DGs operating in a microgrid system architecture to improve the reliability of the system, reduce the cost, and provide better performance. In this technique, one of the DGs in a microgrid system, referred to as a dispatch unit , takes the additional responsibility of mode transitioning to ensure smooth transition and supply/demand balance in the microgrid. In the third technique, an alternative transition technique is proposed through hybridizing the current and droop controllers. The proposed hybrid transition control technique has higher reliability compared to the dispatch unit concept. During the GC mode, the proposed hybrid controller uses current control. During the SA mode, the hybrid controller uses droop control. During the transition mode, both of the controllers participate in formulating the inverter output voltage but with different weights or coefficients. Voltage source inverters interfacing the DGs as well as the proposed transition control algorithms have been modeled to analyze the stability of the algorithms in different configurations. The performances of the proposed algorithms are verified through simulation and experimental studies. It has been found that the proposed control techniques can provide smooth power flow to the local loads during the GC, SA and transition modes.

Channel Deviation-Based Power Control in Body Area Networks.

PubMed

Van, Son Dinh; Cotton, Simon L; Smith, David B

2018-05-01

Internet enabled body area networks (BANs) will form a core part of future remote health monitoring and ambient assisted living technology. In BAN applications, due to the dynamic nature of human activity, the off-body BAN channel can be prone to deep fading caused by body shadowing and multipath fading. Using this knowledge, we present some novel practical adaptive power control protocols based on the channel deviation to simultaneously prolong the lifetime of wearable devices and reduce outage probability. The proposed schemes are both flexible and relatively simple to implement on hardware platforms with constrained resources making them inherently suitable for BAN applications. We present the key algorithm parameters used to dynamically respond to the channel variation. This allows the algorithms to achieve a better energy efficiency and signal reliability in everyday usage scenarios such as those in which a person undertakes many different activities (e.g., sitting, walking, standing, etc.). We also profile their performance against traditional, optimal, and other existing schemes for which it is demonstrated that not only does the outage probability reduce significantly, but the proposed algorithms also save up to average transmit power compared to the competing schemes.

Prognostics of Power Mosfets Under Thermal Stress Accelerated Aging Using Data-Driven and Model-Based Methodologies

NASA Technical Reports Server (NTRS)

Celaya, Jose; Saxena, Abhinav; Saha, Sankalita; Goebel, Kai F.

2011-01-01

An approach for predicting remaining useful life of power MOSFETs (metal oxide field effect transistor) devices has been developed. Power MOSFETs are semiconductor switching devices that are instrumental in electronics equipment such as those used in operation and control of modern aircraft and spacecraft. The MOSFETs examined here were aged under thermal overstress in a controlled experiment and continuous performance degradation data were collected from the accelerated aging experiment. Dieattach degradation was determined to be the primary failure mode. The collected run-to-failure data were analyzed and it was revealed that ON-state resistance increased as die-attach degraded under high thermal stresses. Results from finite element simulation analysis support the observations from the experimental data. Data-driven and model based prognostics algorithms were investigated where ON-state resistance was used as the primary precursor of failure feature. A Gaussian process regression algorithm was explored as an example for a data-driven technique and an extended Kalman filter and a particle filter were used as examples for model-based techniques. Both methods were able to provide valid results. Prognostic performance metrics were employed to evaluate and compare the algorithms.

Analysis of modified SMI method for adaptive array weight control

NASA Technical Reports Server (NTRS)

Dilsavor, R. L.; Moses, R. L.

1989-01-01

An adaptive array is applied to the problem of receiving a desired signal in the presence of weak interference signals which need to be suppressed. A modification, suggested by Gupta, of the sample matrix inversion (SMI) algorithm controls the array weights. In the modified SMI algorithm, interference suppression is increased by subtracting a fraction F of the noise power from the diagonal elements of the estimated covariance matrix. Given the true covariance matrix and the desired signal direction, the modified algorithm is shown to maximize a well-defined, intuitive output power ratio criterion. Expressions are derived for the expected value and variance of the array weights and output powers as a function of the fraction F and the number of snapshots used in the covariance matrix estimate. These expressions are compared with computer simulation and good agreement is found. A trade-off is found to exist between the desired level of interference suppression and the number of snapshots required in order to achieve that level with some certainty. The removal of noise eigenvectors from the covariance matrix inverse is also discussed with respect to this application. Finally, the type and severity of errors which occur in the covariance matrix estimate are characterized through simulation.

Model-free iterative control of repetitive dynamics for high-speed scanning in atomic force microscopy.

PubMed

Li, Yang; Bechhoefer, John

2009-01-01

We introduce an algorithm for calculating, offline or in real time and with no explicit system characterization, the feedforward input required for repetitive motions of a system. The algorithm is based on the secant method of numerical analysis and gives accurate motion at frequencies limited only by the signal-to-noise ratio and the actuator power and range. We illustrate the secant-solver algorithm on a stage used for atomic force microscopy.

Sliding mode controller for a photovoltaic pumping system

NASA Astrophysics Data System (ADS)

ElOugli, A.; Miqoi, S.; Boutouba, M.; Tidhaf, B.

2017-03-01

In this paper, a sliding mode control scheme (SMC) for maximum power point tracking controller for a photovoltaic pumping system, is proposed. The main goal is to maximize the flow rate for a water pump, by forcing the photovoltaic system to operate in its MPP, to obtain the maximum power that a PV system can deliver.And this, through the intermediary of a sliding mode controller to track and control the MPP by overcoming the power oscillation around the operating point, which appears in most implemented MPPT techniques. The sliding mode control approach is recognized as one of the efficient and powerful tools for nonlinear systems under uncertainty conditions.The proposed controller with photovoltaic pumping system is designed and simulated using MATLAB/SIMULINK environment. In addition, to evaluate its performances, a classical MPPT algorithm using perturb and observe (P&O) has been used for the same system to compare to our controller. Simulation results are shown.

Dynamic Model and Control of a Photovoltaic Generation System using Energetic Macroscopic Representation

NASA Astrophysics Data System (ADS)

Solano, Javier; Duarte, José; Vargas, Erwin; Cabrera, Jhon; Jácome, Andrés; Botero, Mónica; Rey, Juan

2016-10-01

This paper addresses the Energetic Macroscopic Representation EMR, the modelling and the control of photovoltaic panel PVP generation systems for simulation purposes. The model of the PVP considers the variations on irradiance and temperature. A maximum power point tracking MPPT algorithm is considered to control the power converter. A novel EMR is proposed to consider the dynamic model of the PVP with variations in the irradiance and the temperature. The EMR is evaluated through simulations of a PVP generation system.

Admission Control Over Internet of Vehicles Attached With Medical Sensors for Ubiquitous Healthcare Applications.

PubMed

Lin, Di; Labeau, Fabrice; Yao, Yuanzhe; Vasilakos, Athanasios V; Tang, Yu

2016-07-01

Wireless technologies and vehicle-mounted or wearable medical sensors are pervasive to support ubiquitous healthcare applications. However, a critical issue of using wireless communications under a healthcare scenario rests at the electromagnetic interference (EMI) caused by radio frequency transmission. A high level of EMI may lead to a critical malfunction of medical sensors, and in such a scenario, a few users who are not transmitting emergency data could be required to reduce their transmit power or even temporarily disconnect from the network in order to guarantee the normal operation of medical sensors as well as the transmission of emergency data. In this paper, we propose a joint power and admission control algorithm to schedule the users' transmission of medical data. The objective of this algorithm is to minimize the number of users who are forced to disconnect from the network while keeping the EMI on medical sensors at an acceptable level. We show that a fixed point of proposed algorithm always exists, and at the fixed point, our proposed algorithm can minimize the number of low-priority users who are required to disconnect from the network. Numerical results illustrate that the proposed algorithm can achieve robust performance against the variations of mobile hospital environments.

Combined magnetic and kinetic control of advanced tokamak steady state scenarios based on semi-empirical modelling

NASA Astrophysics Data System (ADS)

Moreau, D.; Artaud, J. F.; Ferron, J. R.; Holcomb, C. T.; Humphreys, D. A.; Liu, F.; Luce, T. C.; Park, J. M.; Prater, R.; Turco, F.; Walker, M. L.

2015-06-01

This paper shows that semi-empirical data-driven models based on a two-time-scale approximation for the magnetic and kinetic control of advanced tokamak (AT) scenarios can be advantageously identified from simulated rather than real data, and used for control design. The method is applied to the combined control of the safety factor profile, q(x), and normalized pressure parameter, βN, using DIII-D parameters and actuators (on-axis co-current neutral beam injection (NBI) power, off-axis co-current NBI power, electron cyclotron current drive power, and ohmic coil). The approximate plasma response model was identified from simulated open-loop data obtained using a rapidly converging plasma transport code, METIS, which includes an MHD equilibrium and current diffusion solver, and combines plasma transport nonlinearity with 0D scaling laws and 1.5D ordinary differential equations. The paper discusses the results of closed-loop METIS simulations, using the near-optimal ARTAEMIS control algorithm (Moreau D et al 2013 Nucl. Fusion 53 063020) for steady state AT operation. With feedforward plus feedback control, the steady state target q-profile and βN are satisfactorily tracked with a time scale of about 10 s, despite large disturbances applied to the feedforward powers and plasma parameters. The robustness of the control algorithm with respect to disturbances of the H&CD actuators and of plasma parameters such as the H-factor, plasma density and effective charge, is also shown.

Power system distributed oscilation detection based on Synchrophasor data

NASA Astrophysics Data System (ADS)

Ning, Jiawei

Along with increasing demand for electricity, integration of renewable energy and deregulation of power market, power industry is facing unprecedented challenges nowadays. Within the last couple of decades, several serious blackouts have been taking place in United States. As an effective approach to prevent that, power system small signal stability monitoring has been drawing more interests and attentions from researchers. With wide-spread implementation of Synchrophasors around the world in the last decade, power systems real-time online monitoring becomes much more feasible. Comparing with planning study analysis, real-time online monitoring would benefit control room operators immediately and directly. Among all online monitoring methods, Oscillation Modal Analysis (OMA), a modal identification method based on routine measurement data where the input is unmeasured ambient excitation, is a great tool to evaluate and monitor power system small signal stability. Indeed, high sampling Synchrophasor data around power system is fitted perfectly as inputs to OMA. Existing methods in OMA for power systems are all based on centralized algorithms applying at control centers only; however, with rapid growing number of online Synchrophasors the computation burden at control centers is and will be continually exponentially expanded. The increasing computation time at control center compromises the real-time feature of online monitoring. The communication efforts between substation and control center will also be out of reach. Meanwhile, it is difficult or even impossible for centralized algorithms to detect some poorly damped local modes. In order to avert previous shortcomings of centralized OMA methods and embrace the new changes in the power systems, two new distributed oscillation detection methods with two new decentralized structures are presented in this dissertation. Since the new schemes brought substations into the big oscillation detection picture, the proposed methods could achieve faster and more reliable results. Subsequently, this claim is tested and approved by test results of IEEE Two-area simulation test system and real power system historian synchrophasor data case studies.

Development of an MR seat suspension with self-powered generation capability

NASA Astrophysics Data System (ADS)

Sun, S. S.; Ning, D. H.; Yang, J.; Du, H.; Zhang, S. W.; Li, W. H.; Nakano, M.

2017-08-01

This paper proposes a self-powered magnetorheological (MR) seat suspension on the basis of a rotary MR damper and an electromagnetic induction device. By applying the self-powering component to the MR seat suspension, the operation cost of the semi-active seat is much cheaper because no external energy is required to control the MR damper. In this paper, the structure, design and analysis of the seat suspension were presented following the introduction section. The property tests of the self-powered seat suspension were conducted using an MTS machine. A robust control algorithm was developed to control the self-powered MR seat suspension and the vibration attenuation performance of the seat suspension was tested under two different vibration excitations, i.e. harmonic excitation and random excitation. The testing result verifies that the self-powered MR seat suspension under proper control can improve the ride comfort for passengers and drivers.

Different types of maximum power point tracking techniques for renewable energy systems: A survey

NASA Astrophysics Data System (ADS)

Khan, Mohammad Junaid; Shukla, Praveen; Mustafa, Rashid; Chatterji, S.; Mathew, Lini

2016-03-01

Global demand for electricity is increasing while production of energy from fossil fuels is declining and therefore the obvious choice of the clean energy source that is abundant and could provide security for development future is energy from the sun. In this paper, the characteristic of the supply voltage of the photovoltaic generator is nonlinear and exhibits multiple peaks, including many local peaks and a global peak in non-uniform irradiance. To keep global peak, MPPT is the important component of photovoltaic systems. Although many review articles discussed conventional techniques such as P & O, incremental conductance, the correlation ripple control and very few attempts have been made with intelligent MPPT techniques. This document also discusses different algorithms based on fuzzy logic, Ant Colony Optimization, Genetic Algorithm, artificial neural networks, Particle Swarm Optimization Algorithm Firefly, Extremum seeking control method and hybrid methods applied to the monitoring of maximum value of power at point in systems of photovoltaic under changing conditions of irradiance.

Powered Descent Guidance with General Thrust-Pointing Constraints

NASA Technical Reports Server (NTRS)

Carson, John M., III; Acikmese, Behcet; Blackmore, Lars

2013-01-01

The Powered Descent Guidance (PDG) algorithm and software for generating Mars pinpoint or precision landing guidance profiles has been enhanced to incorporate thrust-pointing constraints. Pointing constraints would typically be needed for onboard sensor and navigation systems that have specific field-of-view requirements to generate valid ground proximity and terrain-relative state measurements. The original PDG algorithm was designed to enforce both control and state constraints, including maximum and minimum thrust bounds, avoidance of the ground or descent within a glide slope cone, and maximum speed limits. The thrust-bound and thrust-pointing constraints within PDG are non-convex, which in general requires nonlinear optimization methods to generate solutions. The short duration of Mars powered descent requires guaranteed PDG convergence to a solution within a finite time; however, nonlinear optimization methods have no guarantees of convergence to the global optimal or convergence within finite computation time. A lossless convexification developed for the original PDG algorithm relaxed the non-convex thrust bound constraints. This relaxation was theoretically proven to provide valid and optimal solutions for the original, non-convex problem within a convex framework. As with the thrust bound constraint, a relaxation of the thrust-pointing constraint also provides a lossless convexification that ensures the enhanced relaxed PDG algorithm remains convex and retains validity for the original nonconvex problem. The enhanced PDG algorithm provides guidance profiles for pinpoint and precision landing that minimize fuel usage, minimize landing error to the target, and ensure satisfaction of all position and control constraints, including thrust bounds and now thrust-pointing constraints.

Neural network control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle

NASA Astrophysics Data System (ADS)

Harmon, Frederick G.

2005-11-01

Parallel hybrid-electric propulsion systems would be beneficial for small unmanned aerial vehicles (UAVs) used for military, homeland security, and disaster-monitoring missions. The benefits, due to the hybrid and electric-only modes, include increased time-on-station and greater range as compared to electric-powered UAVs and stealth modes not available with gasoline-powered UAVs. This dissertation contributes to the research fields of small unmanned aerial vehicles, hybrid-electric propulsion system control, and intelligent control. A conceptual design of a small UAV with a parallel hybrid-electric propulsion system is provided. The UAV is intended for intelligence, surveillance, and reconnaissance (ISR) missions. A conceptual design reveals the trade-offs that must be considered to take advantage of the hybrid-electric propulsion system. The resulting hybrid-electric propulsion system is a two-point design that includes an engine primarily sized for cruise speed and an electric motor and battery pack that are primarily sized for a slower endurance speed. The electric motor provides additional power for take-off, climbing, and acceleration and also serves as a generator during charge-sustaining operation or regeneration. The intelligent control of the hybrid-electric propulsion system is based on an instantaneous optimization algorithm that generates a hyper-plane from the nonlinear efficiency maps for the internal combustion engine, electric motor, and lithium-ion battery pack. The hyper-plane incorporates charge-depletion and charge-sustaining strategies. The optimization algorithm is flexible and allows the operator/user to assign relative importance between the use of gasoline, electricity, and recharging depending on the intended mission. A MATLAB/Simulink model was developed to test the control algorithms. The Cerebellar Model Arithmetic Computer (CMAC) associative memory neural network is applied to the control of the UAVs parallel hybrid-electric propulsion system. The CMAC neural network approximates the hyper-plane generated from the instantaneous optimization algorithm and produces torque commands for the internal combustion engine and electric motor. The CMAC neural network controller saves on the required memory as compared to a large look-up table by two orders of magnitude. The CMAC controller also prevents the need to compute a hyper-plane or complex logic every time step.

Digital stroboscopic holographic interferometry for power flow measurements in acoustically driven membranes

NASA Astrophysics Data System (ADS)

Keustermans, William; Pires, Felipe; De Greef, Daniël; Vanlanduit, Steve J. A.; Dirckx, Joris J. J.

2016-06-01

Despite the importance of the eardrum and the ossicles in the hearing chain, it remains an open question how acoustical energy is transmitted between them. Identifying the transmission path at different frequencies could lead to valuable information for the domain of middle ear surgery. In this work a setup for stroboscopic holography is combined with an algorithm for power flow calculations. With our method we were able to accurately locate the power sources and sinks in a membrane. The setup enabled us to make amplitude maps of the out-of-plane displacement of a vibrating rubber membrane at subsequent instances of time within the vibration period. From these, the amplitude maps of the moments of force and velocities are calculated. The magnitude and phase maps are extracted from this amplitude data, and form the input for the power flow calculations. We present the algorithm used for the measurements and for the power flow calculations. Finite element models of a circular plate with a local energy source and sink allowed us to test and optimize this algorithm in a controlled way and without the present of noise, but will not be discussed below. At the setup an earphone was connected with a thin tube which was placed very close to the membrane so that sound impinges locally on the membrane, hereby acting as a local energy source. The energy sink was a little piece of foam carefully placed against the membrane. The laser pulses are fired at selected instants within the vibration period using a 30 mW HeNe continuous wave laser (red light, 632.8 nm) in combination with an acousto-optic modulator. A function generator controls the phase of these illumination pulses and the holograms are recorded using a CCD camera. We present the magnitude and phase maps as well as the power flow measurements on the rubber membrane. Calculation of the divergence of this power flow map provides a simple and fast way of identifying and locating an energy source or sink. In conclusion possible future improvements to the setup and the power flow algorithm are discussed.

Thermal-Aware Test Access Mechanism and Wrapper Design Optimization for System-on-Chips

NASA Astrophysics Data System (ADS)

Yu, Thomas Edison; Yoneda, Tomokazu; Chakrabarty, Krishnendu; Fujiwara, Hideo

Rapid advances in semiconductor manufacturing technology have led to higher chip power densities, which places greater emphasis on packaging and temperature control during testing. For system-on-chips, peak power-based scheduling algorithms have been used to optimize tests under specified power constraints. However, imposing power constraints does not always solve the problem of overheating due to the non-uniform distribution of power across the chip. This paper presents a TAM/Wrapper co-design methodology for system-on-chips that ensures thermal safety while still optimizing the test schedule. The method combines a simplified thermal-cost model with a traditional bin-packing algorithm to minimize test time while satisfying temperature constraints. Furthermore, for temperature checking, thermal simulation is done using cycle-accurate power profiles for more realistic results. Experiments show that even a minimal sacrifice in test time can yield a considerable decrease in test temperature as well as the possibility of further lowering temperatures beyond those achieved using traditional power-based test scheduling.

Smart sensing to drive real-time loads scheduling algorithm in a domotic architecture

NASA Astrophysics Data System (ADS)

Santamaria, Amilcare Francesco; Raimondo, Pierfrancesco; De Rango, Floriano; Vaccaro, Andrea

2014-05-01

Nowadays the focus on power consumption represent a very important factor regarding the reduction of power consumption with correlated costs and the environmental sustainability problems. Automatic control load based on power consumption and use cycle represents the optimal solution to costs restraint. The purpose of these systems is to modulate the power request of electricity avoiding an unorganized work of the loads, using intelligent techniques to manage them based on real time scheduling algorithms. The goal is to coordinate a set of electrical loads to optimize energy costs and consumptions based on the stipulated contract terms. The proposed algorithm use two new main notions: priority driven loads and smart scheduling loads. The priority driven loads can be turned off (stand by) according to a priority policy established by the user if the consumption exceed a defined threshold, on the contrary smart scheduling loads are scheduled in a particular way to don't stop their Life Cycle (LC) safeguarding the devices functions or allowing the user to freely use the devices without the risk of exceeding the power threshold. The algorithm, using these two kind of notions and taking into account user requirements, manages loads activation and deactivation allowing the completion their operation cycle without exceeding the consumption threshold in an off-peak time range according to the electricity fare. This kind of logic is inspired by industrial lean manufacturing which focus is to minimize any kind of power waste optimizing the available resources.

Combustion distribution control using the extremum seeking algorithm

NASA Astrophysics Data System (ADS)

Marjanovic, A.; Krstic, M.; Djurovic, Z.; Kvascev, G.; Papic, V.

2014-12-01

Quality regulation of the combustion process inside the furnace is the basis of high demands for increasing robustness, safety and efficiency of thermal power plants. The paper considers the possibility of spatial temperature distribution control inside the boiler, based on the correction of distribution of coal over the mills. Such control system ensures the maintenance of the flame focus away from the walls of the boiler, and thus preserves the equipment and reduces the possibility of ash slugging. At the same time, uniform heat dissipation over mills enhances the energy efficiency of the boiler, while reducing the pollution of the system. A constrained multivariable extremum seeking algorithm is proposed as a tool for combustion process optimization with the main objective of centralizing the flame in the furnace. Simulations are conducted on a model corresponding to the 350MW boiler of the Nikola Tesla Power Plant, in Obrenovac, Serbia.

Three-phase Four-leg Inverter LabVIEW FPGA Control Code

DOE Office of Scientific and Technical Information (OSTI.GOV)

In the area of power electronics control, Field Programmable Gate Arrays (FPGAs) have the capability to outperform their Digital Signal Processor (DSP) counterparts due to the FPGA’s ability to implement true parallel processing and therefore facilitate higher switching frequencies, higher control bandwidth, and/or enhanced functionality. National Instruments (NI) has developed two platforms, Compact RIO (cRIO) and Single Board RIO (sbRIO), which combine a real-time processor with an FPGA. The FPGA can be programmed with a subset of the well-known LabVIEW graphical programming language. The use of cRIO and sbRIO for power electronics control has developed over the last few yearsmore » to include control of three-phase inverters. Most three-phase inverter topologies include three switching legs. The addition of a fourth-leg to natively generate the neutral connection allows the inverter to serve single-phase loads in a microgrid or stand-alone power system and to balance the three-phase voltages in the presence of significant load imbalance. However, the control of a four-leg inverter is much more complex. In particular, instead of standard two-dimensional space vector modulation (SVM), the inverter requires three-dimensional space vector modulation (3D-SVM). The candidate software implements complete control algorithms in LabVIEW FPGA for a three-phase four-leg inverter. The software includes feedback control loops, three-dimensional space vector modulation gate-drive algorithms, advanced alarm handling capabilities, contactor control, power measurements, and debugging and tuning tools. The feedback control loops allow inverter operation in AC voltage control, AC current control, or DC bus voltage control modes based on external mode selection by a user or supervisory controller. The software includes the ability to synchronize its AC output to the grid or other voltage-source before connection. The software also includes provisions to allow inverter operation in parallel with other voltage regulating devices on the AC or DC buses. This flexibility allows the Inverter to operate as a stand-alone voltage source, connected to the grid, or in parallel with other controllable voltage sources as part of a microgrid or remote power system. In addition, as the inverter is expected to operate under severe unbalanced conditions, the software includes algorithms to accurately compute real and reactive power for each phase based on definitions provided in the IEEE Standard 1459: IEEE Standard Definitions for the Measurement of Electric Power Quantities Under Sinusoidal, Nonsinusoidal, Balanced, or Unbalanced Conditions. Finally, the software includes code to output analog signals for debugging and for tuning of control loops. The software fits on the Xilinx Virtex V LX110 FPGA embedded in the NI cRIO-9118 FPGA chassis, and with a 40 MHz base clock, supports a modulation update rate of 40 MHz, user-settable switching frequencies and synchronized control loop update rates of tens of kHz, and reference waveform generation, including Phase Lock Loop (PLL), update rate of 100 kHz.« less

Woofer-tweeter adaptive optics scanning laser ophthalmoscopic imaging based on Lagrange-multiplier damped least-squares algorithm.

PubMed

Zou, Weiyao; Qi, Xiaofeng; Burns, Stephen A

2011-07-01

We implemented a Lagrange-multiplier (LM)-based damped least-squares (DLS) control algorithm in a woofer-tweeter dual deformable-mirror (DM) adaptive optics scanning laser ophthalmoscope (AOSLO). The algorithm uses data from a single Shack-Hartmann wavefront sensor to simultaneously correct large-amplitude low-order aberrations by a woofer DM and small-amplitude higher-order aberrations by a tweeter DM. We measured the in vivo performance of high resolution retinal imaging with the dual DM AOSLO. We compared the simultaneous LM-based DLS dual DM controller with both single DM controller, and a successive dual DM controller. We evaluated performance using both wavefront (RMS) and image quality metrics including brightness and power spectrum. The simultaneous LM-based dual DM AO can consistently provide near diffraction-limited in vivo routine imaging of human retina.

Computer controlled synchronous shifting of an automatic transmission

DOEpatents

Davis, Roy I.; Patil, Prabhakar B.

1989-01-01

A multiple forward speed automatic transmission produces its lowest forward speed ratio when a hydraulic clutch and hydraulic brake are disengaged and a one-way clutch connects a ring gear to the transmission casing. Second forward speed ratio results when the hydraulic clutch is engaged to connect the ring gear to the planetary carrier of a second gear set. Reverse drive and regenerative operation result when an hydraulic brake fixes the planetary and the direction of power flow is reversed. Various sensors produce signals representing the torque at the output of the transmission or drive wheels, the speed of the power source, and the hydraulic pressure applied to a clutch and brake. A control algorithm produces input data representing a commanded upshift, a commanded downshift, a commanded transmission output torque, and commanded power source speed. A microprocessor processes the inputs and produces a response to them in accordance with the execution of a control algorithm. Output or response signals cause selective engagement and disengagement of the clutch and brake at a rate that satisfies the requirements for a short gear ratio change and smooth torque transfer between the friction elements.

Adaptive control method for core power control in TRIGA Mark II reactor

NASA Astrophysics Data System (ADS)

Sabri Minhat, Mohd; Selamat, Hazlina; Subha, Nurul Adilla Mohd

2018-01-01

The 1MWth Reactor TRIGA PUSPATI (RTP) Mark II type has undergone more than 35 years of operation. The existing core power control uses feedback control algorithm (FCA). It is challenging to keep the core power stable at the desired value within acceptable error bands to meet the safety demand of RTP due to the sensitivity of nuclear research reactor operation. Currently, the system is not satisfied with power tracking performance and can be improved. Therefore, a new design core power control is very important to improve the current performance in tracking and regulate reactor power by control the movement of control rods. In this paper, the adaptive controller and focus on Model Reference Adaptive Control (MRAC) and Self-Tuning Control (STC) were applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, adaptive controller model, and control rods selection programming. The mathematical models of the reactor core were based on point kinetics model, thermal hydraulic models, and reactivity models. The adaptive control model was presented using Lyapunov method to ensure stable close loop system and STC Generalised Minimum Variance (GMV) Controller was not necessary to know the exact plant transfer function in designing the core power control. The performance between proposed adaptive control and FCA will be compared via computer simulation and analysed the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.

Optimization of hydraulic turbine governor parameters based on WPA

NASA Astrophysics Data System (ADS)

Gao, Chunyang; Yu, Xiangyang; Zhu, Yong; Feng, Baohao

2018-01-01

The parameters of hydraulic turbine governor directly affect the dynamic characteristics of the hydraulic unit, thus affecting the regulation capacity and the power quality of power grid. The governor of conventional hydropower unit is mainly PID governor with three adjustable parameters, which are difficult to set up. In order to optimize the hydraulic turbine governor, this paper proposes wolf pack algorithm (WPA) for intelligent tuning since the good global optimization capability of WPA. Compared with the traditional optimization method and PSO algorithm, the results show that the PID controller designed by WPA achieves a dynamic quality of hydraulic system and inhibits overshoot.

Real-Time Neural Signals Decoding onto Off-the-Shelf DSP Processors for Neuroprosthetic Applications.

PubMed

Pani, Danilo; Barabino, Gianluca; Citi, Luca; Meloni, Paolo; Raspopovic, Stanisa; Micera, Silvestro; Raffo, Luigi

2016-09-01

The control of upper limb neuroprostheses through the peripheral nervous system (PNS) can allow restoring motor functions in amputees. At present, the important aspect of the real-time implementation of neural decoding algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited hardware resources have on the efficiency/effectiveness of any given algorithm. Present study is addressing the optimization of a template matching based algorithm for PNS signals decoding that is a milestone for its real-time, full implementation onto a floating-point digital signal processor (DSP). The proposed optimized real-time algorithm achieves up to 96% of correct classification on real PNS signals acquired through LIFE electrodes on animals, and can correctly sort spikes of a synthetic cortical dataset with sufficiently uncorrelated spike morphologies (93% average correct classification) comparably to the results obtained with top spike sorter (94% on average on the same dataset). The power consumption enables more than 24 h processing at the maximum load, and latency model has been derived to enable a fair performance assessment. The final embodiment demonstrates the real-time performance onto a low-power off-the-shelf DSP, opening to experiments exploiting the efferent signals to control a motor neuroprosthesis.

A Maximum Power Point Tracking Control Method of a Photovoltaic Power Generator with Consideration of Dynamic Characteristics of Solar Cells

NASA Astrophysics Data System (ADS)

Watanabe, Takashi; Yoshida, Toshiya; Ohniwa, Katsumi

This paper discusses a new control strategy for photovoltaic power generation systems with consideration of dynamic characteristics of the photovoltaic cells. The controller estimates internal currents of an equivalent circuit for the cells. This estimated, or the virtual current and the actual voltage of the cells are fed to a conventional Maximum-Power-Point-Tracking (MPPT) controller. Consequently, this MPPT controller still tracks the optimum point even though it is so designed that the seeking speed of the operating point is extremely high. This system may suit for applications, which are installed in rapidly changeable insolation and temperature-conditions e.g. automobiles, trains, and airplanes. The proposed method is verified by experiment with a combination of this estimating function and the modified Boehringer's MPPT algorithm.

The Design and Development of the SMEX-Lite Power System

NASA Technical Reports Server (NTRS)

Rakow, Glenn P.; Schnurr, Richard G., Jr.; Solly, Michael A.

1998-01-01

This paper describes the design and development of a 250W orbit average electrical power system electronic Power Node and software for use in Low Earth Orbit missions. The mass of the Power Node is 3.6 Kg (8 lb.). The dimensions of the Power Node are 30cm x 26cm x 7.9cm (11 in. x 10.25 in x 3.1 in.) The design was realized using software, Field Programmable Gate Array (FPGA) digital logic and surface mount technology. The design is generic enough to reduce the non-recurring engineering for different mission configurations. The Power Node charges one to five, low cost, 22-cell 4 AH D-cell battery packs independently. The battery charging algorithms are executed in the power software to reduce the mass and size of the power electronic. The Power Node implements a peak-power tracking algorithm using an innovative hardware/software approach. The power software task is hosted on the spacecraft processor. The power software task generates a MIL-STD-1553 command packet to update the Power Node control settings. The settings for the battery voltage and current limits, as well as minimum solar array voltage used to implement peak power tracking are contained in this packet. Several advanced topologies are used in the Power Node. These include synchronous rectification in the bus regulators, average current control in the battery chargers and quasi-resonant converters for the Field Effect Transistor (FET) transistor drive electronics. Lastly, the main bus regulator uses a feed-forward topology with the PWM implemented in an FPGA.

A Standalone Solar Photovoltaic Power Generation using Cuk Converter and Single Phase Inverter

NASA Astrophysics Data System (ADS)

Verma, A. K.; Singh, B.; Kaushika, S. C.

2013-03-01

In this paper, a standalone solar photovoltaic (SPV) power generating system is designed and modeled using a Cuk dc-dc converter and a single phase voltage source inverter (VSI). In this system, a dc-dc boost converter boosts a low voltage of a PV array to charge a battery at 24 V using a maximum power point tracking control algorithm. To step up a 24 V battery voltage to 360 V dc, a high frequency transformer based isolated dc-dc Cuk converter is used to reduce size, weight and losses. The dc voltage of 360 V is fed to a single phase VSI with unipolar switching to achieve a 230 Vrms, 50 Hz ac. The main objectives of this investigation are on efficiency improvement, reduction in cost, weight and size of the system and to provide an uninterruptible power to remotely located consumers. The complete SPV system is designed and it is modeled in MATLAB/Simulink. The simulated results are presented to demonstrate its satisfactory performance for validating the proposed design and control algorithm.

A Lyapunov based approach to energy maximization in renewable energy technologies

NASA Astrophysics Data System (ADS)

Iyasere, Erhun

This dissertation describes the design and implementation of Lyapunov-based control strategies for the maximization of the power captured by renewable energy harnessing technologies such as (i) a variable speed, variable pitch wind turbine, (ii) a variable speed wind turbine coupled to a doubly fed induction generator, and (iii) a solar power generating system charging a constant voltage battery. First, a torque control strategy is presented to maximize wind energy captured in variable speed, variable pitch wind turbines at low to medium wind speeds. The proposed strategy applies control torque to the wind turbine pitch and rotor subsystems to simultaneously control the blade pitch and tip speed ratio, via the rotor angular speed, to an optimum point at which the capture efficiency is maximum. The control method allows for aerodynamic rotor power maximization without exact knowledge of the wind turbine model. A series of numerical results show that the wind turbine can be controlled to achieve maximum energy capture. Next, a control strategy is proposed to maximize the wind energy captured in a variable speed wind turbine, with an internal induction generator, at low to medium wind speeds. The proposed strategy controls the tip speed ratio, via the rotor angular speed, to an optimum point at which the efficiency constant (or power coefficient) is maximal for a particular blade pitch angle and wind speed by using the generator rotor voltage as a control input. This control method allows for aerodynamic rotor power maximization without exact wind turbine model knowledge. Representative numerical results demonstrate that the wind turbine can be controlled to achieve near maximum energy capture. Finally, a power system consisting of a photovoltaic (PV) array panel, dc-to-dc switching converter, charging a battery is considered wherein the environmental conditions are time-varying. A backstepping PWM controller is developed to maximize the power of the solar generating system. The controller tracks a desired array voltage, designed online using an incremental conductance extremum-seeking algorithm, by varying the duty cycle of the switching converter. The stability of the control algorithm is demonstrated by means of Lyapunov analysis. Representative numerical results demonstrate that the grid power system can be controlled to track the maximum power point of the photovoltaic array panel in varying atmospheric conditions. Additionally, the performance of the proposed strategy is compared to the typical maximum power point tracking (MPPT) method of perturb and observe (P&O), where the converter dynamics are ignored, and is shown to yield better results.

DOE Office of Scientific and Technical Information (OSTI.GOV)

D.A. Gates; J.R. Ferron; M. Bell

In 2003, the NSTX plasma control system was used for plasma shape control using real-time equilibrium reconstruction (using the rtEFIT code - J. Ferron, et al., Nucl. Fusion 38 1055 (1998)). rtEFIT is now in routine use for plasma boundary control [D. A. Gates, et al., submitted to Nuclear Fusion (2005)]. More recently, the system has been upgraded to support feedback control of the resistive wall mode (RWM). This paper describes the hardware and software improvements that were made in support of these physics requirements. The real-time data acquisition system now acquires 352 channels of data at 5kHz for eachmore » NSTX plasma discharge. The latency for the data acquisition, which uses the FPDP (Front Panel Data Port) protocol, is measured to be {approx}8 microseconds. A Stand-Alone digitizer (SAD), designed at PPPL, along with an FPDP Input multiplexing module (FIMM) allows for simple modular upgrades. An interface module was built to interface between the FPDP output of the NSTX control system and the legacy Power Conversion link (PCLINK) used for communicating with the PPPL power supplies (first used for TFTR). Additionally a module has been built for communicating with the switching power amplifiers (SPA) recently installed on NSTX. In addition to the hardware developments, the control software [D. Mastrovito, Fusion Eng. And Design 71 65 (2004)] on the NSTX control system has been upgraded. The control computer is an eight processor (8x333MHz G4) built by Sky Computers (Helmsford, MA). The device driver software for the hardware described above will be discussed, as well as the new control algorithms that have been developed to control the switching power supplies for RWM control. An important initial task in RWM feedback is to develop a reliable mode detection algorithm.« less

Fuzzy control of power converters based on quasilinear modelling

NASA Astrophysics Data System (ADS)

Li, C. K.; Lee, W. L.; Chou, Y. W.

1995-03-01

Unlike feedback control by the fuzzy PID method, a new fuzzy control algorithm based on quasilinear modelling of the DC-DC converter is proposed. Investigation is carried out using a buck-boost converter. Simulation results demonstrated that the converter can be regulated with improved performance even when subjected to input disturbance and load variation.

Robust Group Sparse Beamforming for Multicast Green Cloud-RAN With Imperfect CSI

NASA Astrophysics Data System (ADS)

Shi, Yuanming; Zhang, Jun; Letaief, Khaled B.

2015-09-01

In this paper, we investigate the network power minimization problem for the multicast cloud radio access network (Cloud-RAN) with imperfect channel state information (CSI). The key observation is that network power minimization can be achieved by adaptively selecting active remote radio heads (RRHs) via controlling the group-sparsity structure of the beamforming vector. However, this yields a non-convex combinatorial optimization problem, for which we propose a three-stage robust group sparse beamforming algorithm. In the first stage, a quadratic variational formulation of the weighted mixed l1/l2-norm is proposed to induce the group-sparsity structure in the aggregated beamforming vector, which indicates those RRHs that can be switched off. A perturbed alternating optimization algorithm is then proposed to solve the resultant non-convex group-sparsity inducing optimization problem by exploiting its convex substructures. In the second stage, we propose a PhaseLift technique based algorithm to solve the feasibility problem with a given active RRH set, which helps determine the active RRHs. Finally, the semidefinite relaxation (SDR) technique is adopted to determine the robust multicast beamformers. Simulation results will demonstrate the convergence of the perturbed alternating optimization algorithm, as well as, the effectiveness of the proposed algorithm to minimize the network power consumption for multicast Cloud-RAN.

New human-centered linear and nonlinear motion cueing algorithms for control of simulator motion systems

NASA Astrophysics Data System (ADS)

Telban, Robert J.

While the performance of flight simulator motion system hardware has advanced substantially, the development of the motion cueing algorithm, the software that transforms simulated aircraft dynamics into realizable motion commands, has not kept pace. To address this, new human-centered motion cueing algorithms were developed. A revised "optimal algorithm" uses time-invariant filters developed by optimal control, incorporating human vestibular system models. The "nonlinear algorithm" is a novel approach that is also formulated by optimal control, but can also be updated in real time. It incorporates a new integrated visual-vestibular perception model that includes both visual and vestibular sensation and the interaction between the stimuli. A time-varying control law requires the matrix Riccati equation to be solved in real time by a neurocomputing approach. Preliminary pilot testing resulted in the optimal algorithm incorporating a new otolith model, producing improved motion cues. The nonlinear algorithm vertical mode produced a motion cue with a time-varying washout, sustaining small cues for longer durations and washing out large cues more quickly compared to the optimal algorithm. The inclusion of the integrated perception model improved the responses to longitudinal and lateral cues. False cues observed with the NASA adaptive algorithm were absent. As a result of unsatisfactory sensation, an augmented turbulence cue was added to the vertical mode for both the optimal and nonlinear algorithms. The relative effectiveness of the algorithms, in simulating aircraft maneuvers, was assessed with an eleven-subject piloted performance test conducted on the NASA Langley Visual Motion Simulator (VMS). Two methods, the quasi-objective NASA Task Load Index (TLX), and power spectral density analysis of pilot control, were used to assess pilot workload. TLX analysis reveals, in most cases, less workload and variation among pilots with the nonlinear algorithm. Control input analysis shows pilot-induced oscillations on a straight-in approach are less prevalent compared to the optimal algorithm. The augmented turbulence cues increased workload on an offset approach that the pilots deemed more realistic compared to the NASA adaptive algorithm. The takeoff with engine failure showed the least roll activity for the nonlinear algorithm, with the least rudder pedal activity for the optimal algorithm.

Spectrum and power allocation in cognitive multi-beam satellite communications with flexible satellite payloads

NASA Astrophysics Data System (ADS)

Liu, Zhihui; Wang, Haitao; Dong, Tao; Yin, Jie; Zhang, Tingting; Guo, Hui; Li, Dequan

2018-02-01

In this paper, the cognitive multi-beam satellite system, i.e., two satellite networks coexist through underlay spectrum sharing, is studied, and the power and spectrum allocation method is employed for interference control and throughput maximization. Specifically, the multi-beam satellite with flexible payload reuses the authorized spectrum of the primary satellite, adjusting its transmission band as well as power for each beam to limit its interference on the primary satellite below the prescribed threshold and maximize its own achievable rate. This power and spectrum allocation problem is formulated as a mixed nonconvex programming. For effective solving, we first introduce the concept of signal to leakage plus noise ratio (SLNR) to decouple multiple transmit power variables in the both objective and constraint, and then propose a heuristic algorithm to assign spectrum sub-bands. After that, a stepwise plus slice-wise algorithm is proposed to implement the discrete power allocation. Finally, simulation results show that adopting cognitive technology can improve spectrum efficiency of the satellite communication.

Region-Oriented Placement Algorithm for Coarse-Grained Power-Gating FPGA Architecture

NASA Astrophysics Data System (ADS)

Li, Ce; Dong, Yiping; Watanabe, Takahiro

An FPGA plays an essential role in industrial products due to its fast, stable and flexible features. But the power consumption of FPGAs used in portable devices is one of critical issues. Top-down hierarchical design method is commonly used in both ASIC and FPGA design. But, in the case where plural modules are integrated in an FPGA and some of them might be in sleep-mode, current FPGA architecture cannot be fully effective. In this paper, coarse-grained power gating FPGA architecture is proposed where a whole area of an FPGA is partitioned into several regions and power supply is controlled for each region, so that modules in sleep mode can be effectively power-off. We also propose a region oriented FPGA placement algorithm fitted to this user's hierarchical design based on VPR[1]. Simulation results show that this proposed method could reduce power consumption of FPGA by 38% on average by setting unused modules or regions in sleep mode.

Design of PID temperature control system based on STM32

NASA Astrophysics Data System (ADS)

Zhang, Jianxin; Li, Hailin; Ma, Kai; Xue, Liang; Han, Bianhua; Dong, Yuemeng; Tan, Yue; Gu, Chengru

2018-03-01

A rapid and high-accuracy temperature control system was designed using proportional-integral-derivative (PID) control algorithm with STM32 as micro-controller unit (MCU). The temperature control system can be applied in the fields which have high requirements on the response speed and accuracy of temperature control. The temperature acquisition circuit in system adopted Pt1000 resistance thermometer as temperature sensor. Through this acquisition circuit, the monitoring actual temperature signal could be converted into voltage signal and transmitted into MCU. A TLP521-1 photoelectric coupler was matched with BD237 power transistor to drive the thermoelectric cooler (TEC) in FTA951 module. The effective electric power of TEC was controlled by the pulse width modulation (PWM) signals which generated by MCU. The PWM signal parameters could be adjusted timely by PID algorithm according to the difference between monitoring actual temperature and set temperature. The upper computer was used to input the set temperature and monitor the system running state via serial port. The application experiment results show that the temperature control system is featured by simple structure, rapid response speed, good stability and high temperature control accuracy with the error less than ±0.5°C.

Real-time path planning and autonomous control for helicopter autorotation

NASA Astrophysics Data System (ADS)

Yomchinda, Thanan

Autorotation is a descending maneuver that can be used to recover helicopters in the event of total loss of engine power; however it is an extremely difficult and complex maneuver. The objective of this work is to develop a real-time system which provides full autonomous control for autorotation landing of helicopters. The work includes the development of an autorotation path planning method and integration of the path planner with a primary flight control system. The trajectory is divided into three parts: entry, descent and flare. Three different optimization algorithms are used to generate trajectories for each of these segments. The primary flight control is designed using a linear dynamic inversion control scheme, and a path following control law is developed to track the autorotation trajectories. Details of the path planning algorithm, trajectory following control law, and autonomous autorotation system implementation are presented. The integrated system is demonstrated in real-time high fidelity simulations. Results indicate feasibility of the capability of the algorithms to operate in real-time and of the integrated systems ability to provide safe autorotation landings. Preliminary simulations of autonomous autorotation on a small UAV are presented which will lead to a final hardware demonstration of the algorithms.

Isolated Power Generation System Using Permanent Magnet Synchronous Generator with Improved Power Quality

NASA Astrophysics Data System (ADS)

Arya, Sabha Raj; Patel, Ashish; Giri, Ashutosh

2018-06-01

This paper deals wind energy based power generation system using Permanent Magnet Synchronous Generator (PMSG). It is controlled using advanced enhanced phase-lock loop for power quality features using distribution static compensator to eliminate the harmonics and to provide KVAR compensation as well as load balancing. It also manages rated potential at the point of common interface under linear and non-linear loads. In order to have better efficiency and reliable operation of PMSG driven by wind turbine, it is necessary to analyze the governing equation of wind based turbine and PMSG under fixed and variable wind speed. For handling power quality problems, power electronics based shunt connected custom power device is used in three wire system. The simulations in MATLAB/Simulink environment have been carried out in order to demonstrate this model and control approach used for the power quality enhancement. The performance results show the adequate performance of PMSG based power generation system and control algorithm.

Isolated Power Generation System Using Permanent Magnet Synchronous Generator with Improved Power Quality

NASA Astrophysics Data System (ADS)

Arya, Sabha Raj; Patel, Ashish; Giri, Ashutosh

2018-03-01

This paper deals wind energy based power generation system using Permanent Magnet Synchronous Generator (PMSG). It is controlled using advanced enhanced phase-lock loop for power quality features using distribution static compensator to eliminate the harmonics and to provide KVAR compensation as well as load balancing. It also manages rated potential at the point of common interface under linear and non-linear loads. In order to have better efficiency and reliable operation of PMSG driven by wind turbine, it is necessary to analyze the governing equation of wind based turbine and PMSG under fixed and variable wind speed. For handling power quality problems, power electronics based shunt connected custom power device is used in three wire system. The simulations in MATLAB/Simulink environment have been carried out in order to demonstrate this model and control approach used for the power quality enhancement. The performance results show the adequate performance of PMSG based power generation system and control algorithm.

Microcomputer technology applications: Charger and regulator software for a breadboard programmable power processor

NASA Technical Reports Server (NTRS)

Green, D. M.

1978-01-01

Software programs are described, one which implements a voltage regulation function, and one which implements a charger function with peak-power tracking of its input. The software, written in modular fashion, is intended as a vehicle for further experimentation with the P-3 system. A control teleprinter allows an operator to make parameter modifications to the control algorithm during experiments. The programs require 3K ROM and 2K ram each. User manuals for each system are included as well as a third program for simple I/O control.

Resilient guaranteed cost control of a power system.

PubMed

Soliman, Hisham M; Soliman, Mostafa H; Hassan, Mohammad F

2014-05-01

With the development of power system interconnection, the low-frequency oscillation is becoming more and more prominent which may cause system separation and loss of energy to consumers. This paper presents an innovative robust control for power systems in which the operating conditions are changing continuously due to load changes. However, practical implementation of robust control can be fragile due to controller inaccuracies (tolerance of resistors used with operational amplifiers). A new design of resilient (non-fragile) robust control is given that takes into consideration both model and controller uncertainties by an iterative solution of a set of linear matrix inequalities (LMI). Both uncertainties are cast into a norm-bounded structure. A sufficient condition is derived to achieve the desired settling time for damping power system oscillations in face of plant and controller uncertainties. Furthermore, an improved controller design, resilient guaranteed cost controller, is derived to achieve oscillations damping in a guaranteed cost manner. The effectiveness of the algorithm is shown for a single machine infinite bus system, and then, it is extended to multi-area power system.

Multi-objective optimization of MOSFETs channel widths and supply voltage in the proposed dual edge-triggered static D flip-flop with minimum average power and delay by using fuzzy non-dominated sorting genetic algorithm-II.

PubMed

Keivanian, Farshid; Mehrshad, Nasser; Bijari, Abolfazl

2016-01-01

D Flip-Flop as a digital circuit can be used as a timing element in many sophisticated circuits. Therefore the optimum performance with the lowest power consumption and acceptable delay time will be critical issue in electronics circuits. The newly proposed Dual-Edge Triggered Static D Flip-Flop circuit layout is defined as a multi-objective optimization problem. For this, an optimum fuzzy inference system with fuzzy rules is proposed to enhance the performance and convergence of non-dominated sorting Genetic Algorithm-II by adaptive control of the exploration and exploitation parameters. By using proposed Fuzzy NSGA-II algorithm, the more optimum values for MOSFET channel widths and power supply are discovered in search space than ordinary NSGA types. What is more, the design parameters involving NMOS and PMOS channel widths and power supply voltage and the performance parameters including average power consumption and propagation delay time are linked. To do this, the required mathematical backgrounds are presented in this study. The optimum values for the design parameters of MOSFETs channel widths and power supply are discovered. Based on them the power delay product quantity (PDP) is 6.32 PJ at 125 MHz Clock Frequency, L = 0.18 µm, and T = 27 °C.

Development of a Two-Wheel Contingency Mode for the MAP Spacecraft

NASA Technical Reports Server (NTRS)

Starin, Scott R.; ODonnell, James R., Jr.; Bauer, Frank (Technical Monitor)

2002-01-01

The Microwave Anisotropy Probe (MAP) is a follow-on mission to the Cosmic Background Explorer (COBE), and is currently collecting data from its orbit near the second Sun-Earth libration point. Due to limited mass, power, and financial resources, a traditional reliability concept including fully redundant components was not feasible for MAP. Instead, the MAP design employs selective hardware redundancy in tandem with contingency software modes and algorithms to improve the odds of mission success. One direction for such improvement has been the development of a two-wheel backup control strategy. This strategy would allow MAP to position itself for maneuvers and collect science data should one of its three reaction wheels fail. Along with operational considerations, the strategy includes three new control algorithms. These algorithms would use the remaining attitude control actuators-thrusters and two reaction wheels-in ways that achieve control goals while minimizing adverse impacts on the functionality of other subsystems and software.

Power in the loop real time simulation platform for renewable energy generation

NASA Astrophysics Data System (ADS)

Li, Yang; Shi, Wenhui; Zhang, Xing; He, Guoqing

2018-02-01

Nowadays, a large scale of renewable energy sources has been connecting to power system and the real time simulation platform is widely used to carry out research on integration control algorithm, power system stability etc. Compared to traditional pure digital simulation and hardware in the loop simulation, power in the loop simulation has higher accuracy and degree of reliability. In this paper, a power in the loop analog digital hybrid simulation platform has been built and it can be used not only for the single generation unit connecting to grid, but also for multiple new energy generation units connecting to grid. A wind generator inertia control experiment was carried out on the platform. The structure of the inertia control platform was researched and the results verify that the platform is up to need for renewable power in the loop real time simulation.

A decision tree-based on-line preventive control strategy for power system transient instability prevention

NASA Astrophysics Data System (ADS)

Xu, Yan; Dong, Zhao Yang; Zhang, Rui; Wong, Kit Po

2014-02-01

Maintaining transient stability is a basic requirement for secure power system operations. Preventive control deals with modifying the system operating point to withstand probable contingencies. In this article, a decision tree (DT)-based on-line preventive control strategy is proposed for transient instability prevention of power systems. Given a stability database, a distance-based feature estimation algorithm is first applied to identify the critical generators, which are then used as features to develop a DT. By interpreting the splitting rules of DT, preventive control is realised by formulating the rules in a standard optimal power flow model and solving it. The proposed method is transparent in control mechanism, on-line computation compatible and convenient to deal with multi-contingency. The effectiveness and efficiency of the method has been verified on New England 10-machine 39-bus test system.

Optimal tracking and second order sliding power control of the DFIG wind turbine

NASA Astrophysics Data System (ADS)

Abdeddaim, S.; Betka, A.; Charrouf, O.

2017-02-01

In the present paper, an optimal operation of a grid-connected variable speed wind turbine equipped with a Doubly Fed Induction Generator (DFIG) is presented. The proposed cascaded nonlinear controller is designed to perform two main objectives. In the outer loop, a maximum power point tracking (MPPT) algorithm based on fuzzy logic theory is designed to permanently extract the optimal aerodynamic energy, whereas in the inner loop, a second order sliding mode control (2-SM) is applied to achieve smooth regulation of both stator active and reactive powers quantities. The obtained simulation results show a permanent track of the MPP point regardless of the turbine power-speed slope moreover the proposed sliding mode control strategy presents attractive features such as chattering-free, compared to the conventional first order sliding technique (1-SM).

Design of intelligent vehicle control system based on single chip microcomputer

NASA Astrophysics Data System (ADS)

Zhang, Congwei

2018-06-01

The smart car microprocessor uses the KL25ZV128VLK4 in the Freescale series of single-chip microcomputers. The image sampling sensor uses the CMOS digital camera OV7725. The obtained track data is processed by the corresponding algorithm to obtain track sideline information. At the same time, the pulse width modulation control (PWM) is used to control the motor and servo movements, and based on the digital incremental PID algorithm, the motor speed control and servo steering control are realized. In the project design, IAR Embedded Workbench IDE is used as the software development platform to program and debug the micro-control module, camera image processing module, hardware power distribution module, motor drive and servo control module, and then complete the design of the intelligent car control system.

Learning in engineered multi-agent systems

NASA Astrophysics Data System (ADS)

Menon, Anup

Consider the problem of maximizing the total power produced by a wind farm. Due to aerodynamic interactions between wind turbines, each turbine maximizing its individual power---as is the case in present-day wind farms---does not lead to optimal farm-level power capture. Further, there are no good models to capture the said aerodynamic interactions, rendering model based optimization techniques ineffective. Thus, model-free distributed algorithms are needed that help turbines adapt their power production on-line so as to maximize farm-level power capture. Motivated by such problems, the main focus of this dissertation is a distributed model-free optimization problem in the context of multi-agent systems. The set-up comprises of a fixed number of agents, each of which can pick an action and observe the value of its individual utility function. An individual's utility function may depend on the collective action taken by all agents. The exact functional form (or model) of the agent utility functions, however, are unknown; an agent can only measure the numeric value of its utility. The objective of the multi-agent system is to optimize the welfare function (i.e. sum of the individual utility functions). Such a collaborative task requires communications between agents and we allow for the possibility of such inter-agent communications. We also pay attention to the role played by the pattern of such information exchange on certain aspects of performance. We develop two algorithms to solve this problem. The first one, engineered Interactive Trial and Error Learning (eITEL) algorithm, is based on a line of work in the Learning in Games literature and applies when agent actions are drawn from finite sets. While in a model-free setting, we introduce a novel qualitative graph-theoretic framework to encode known directed interactions of the form "which agents' action affect which others' payoff" (interaction graph). We encode explicit inter-agent communications in a directed graph (communication graph) and, under certain conditions, prove convergence of agent joint action (under eITEL) to the welfare optimizing set. The main condition requires that the union of interaction and communication graphs be strongly connected; thus the algorithm combines an implicit form of communication (via interactions through utility functions) with explicit inter-agent communications to achieve the given collaborative goal. This work has kinship with certain evolutionary computation techniques such as Simulated Annealing; the algorithm steps are carefully designed such that it describes an ergodic Markov chain with a stationary distribution that has support over states where agent joint actions optimize the welfare function. The main analysis tool is perturbed Markov chains and results of broader interest regarding these are derived as well. The other algorithm, Collaborative Extremum Seeking (CES), uses techniques from extremum seeking control to solve the problem when agent actions are drawn from the set of real numbers. In this case, under the assumption of existence of a local minimizer for the welfare function and a connected undirected communication graph between agents, a result regarding convergence of joint action to a small neighborhood of a local optimizer of the welfare function is proved. Since extremum seeking control uses a simultaneous gradient estimation-descent scheme, gradient information available in the continuous action space formulation is exploited by the CES algorithm to yield improved convergence speeds. The effectiveness of this algorithm for the wind farm power maximization problem is evaluated via simulations. Lastly, we turn to a different question regarding role of the information exchange pattern on performance of distributed control systems by means of a case study for the vehicle platooning problem. In the vehicle platoon control problem, the objective is to design distributed control laws for individual vehicles in a platoon (or a road-train) that regulate inter-vehicle distances at a specified safe value while the entire platoon follows a leader-vehicle. While most of the literature on the problem deals with some inadequacy in control performance when the information exchange is of the nearest neighbor-type, we consider an arbitrary graph serving as information exchange pattern and derive a relationship between how a certain indicator of control performance is related to the information pattern. Such analysis helps in understanding qualitative features of the `right' information pattern for this problem.

Pump and Flow Control Subassembly of Thermal Control Subsystem for Photovoltaic Power Module

NASA Technical Reports Server (NTRS)

Motil, Brian; Santen, Mark A.

1993-01-01

The pump and flow control subassembly (PFCS) is an orbital replacement unit (ORU) on the Space Station Freedom photovoltaic power module (PVM). The PFCS pumps liquid ammonia at a constant rate of approximately 1170 kg/hr while providing temperature control by flow regulation between the radiator and the bypass loop. Also, housed within the ORU is an accumulator to compensate for fluid volumetric changes as well as the electronics and firmware for monitoring and control of the photovoltaic thermal control system (PVTCS). Major electronic functions include signal conditioning, data interfacing and motor control. This paper will provide a description of each major component within the PFCS along with performance test data. In addition, this paper will discuss the flow control algorithm and describe how the nickel hydrogen batteries and associated power electronics will be thermally controlled through regulation of coolant flow to the radiator.

A synergistic method for vibration suppression of an elevator mechatronic system

NASA Astrophysics Data System (ADS)

Knezevic, Bojan Z.; Blanusa, Branko; Marcetic, Darko P.

2017-10-01

Modern elevators are complex mechatronic systems which have to satisfy high performance in precision, safety and ride comfort. Each elevator mechatronic system (EMS) contains a mechanical subsystem which is characterized by its resonant frequency. In order to achieve high performance of the whole system, the control part of the EMS inevitably excites resonant circuits causing the occurrence of vibration. This paper proposes a synergistic solution based on the jerk control and the upgrade of the speed controller with a band-stop filter to restore lost ride comfort and speed control caused by vibration. The band-stop filter eliminates the resonant component from the speed controller spectra and jerk control provides operating of the speed controller in a linear mode as well as increased ride comfort. The original method for band-stop filter tuning based on Goertzel algorithm and Kiefer search algorithm is proposed in this paper. In order to generate the speed reference trajectory which can be defined by different shapes and amplitudes of jerk, a unique generalized model is proposed. The proposed algorithm is integrated in the power drive control algorithm and implemented on the digital signal processor. Through experimental verifications on a scale down prototype of the EMS it has been verified that only synergistic effect of controlling jerk and filtrating the reference torque can completely eliminate vibrations.

Optimal Refueling Pattern Search for a CANDU Reactor Using a Genetic Algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Quang Binh, DO; Gyuhong, ROH; Hangbok, CHOI

2006-07-01

This paper presents the results from the application of genetic algorithms to a refueling optimization of a Canada deuterium uranium (CANDU) reactor. This work aims at making a mathematical model of the refueling optimization problem including the objective function and constraints and developing a method based on genetic algorithms to solve the problem. The model of the optimization problem and the proposed method comply with the key features of the refueling strategy of the CANDU reactor which adopts an on-power refueling operation. In this study, a genetic algorithm combined with an elitism strategy was used to automatically search for themore » refueling patterns. The objective of the optimization was to maximize the discharge burn-up of the refueling bundles, minimize the maximum channel power, or minimize the maximum change in the zone controller unit (ZCU) water levels. A combination of these objectives was also investigated. The constraints include the discharge burn-up, maximum channel power, maximum bundle power, channel power peaking factor and the ZCU water level. A refueling pattern that represents the refueling rate and channels was coded by a one-dimensional binary chromosome, which is a string of binary numbers 0 and 1. A computer program was developed in FORTRAN 90 running on an HP 9000 workstation to conduct the search for the optimal refueling patterns for a CANDU reactor at the equilibrium state. The results showed that it was possible to apply genetic algorithms to automatically search for the refueling channels of the CANDU reactor. The optimal refueling patterns were compared with the solutions obtained from the AUTOREFUEL program and the results were consistent with each other. (authors)« less

Research on charging and discharging control strategy for electric vehicles as distributed energy storage devices

NASA Astrophysics Data System (ADS)

Zhang, Min; Yang, Feng; Zhang, Dongqing; Tang, Pengcheng

2018-02-01

A large number of electric vehicles are connected to the family micro grid will affect the operation safety of the power grid and the quality of power. Considering the factors of family micro grid price and electric vehicle as a distributed energy storage device, a two stage optimization model is established, and the improved discrete binary particle swarm optimization algorithm is used to optimize the parameters in the model. The proposed control strategy of electric vehicle charging and discharging is of practical significance for the rational control of electric vehicle as a distributed energy storage device and electric vehicle participating in the peak load regulation of power consumption.

Low power sensor network for wireless condition monitoring

NASA Astrophysics Data System (ADS)

Richter, Ch.; Frankenstein, B.; Schubert, L.; Weihnacht, B.; Friedmann, H.; Ebert, C.

2009-03-01

For comprehensive fatigue tests and surveillance of large scale structures, a vibration monitoring system working in the Hz and sub Hz frequency range was realized and tested. The system is based on a wireless sensor network and focuses especially on the realization of a low power measurement, signal processing and communication. Regarding the development, we met the challenge of synchronizing the wireless connected sensor nodes with sufficient accuracy. The sensor nodes ware realized by compact, sensor near signal processing structures containing components for analog preprocessing of acoustic signals, their digitization, algorithms for data reduction and network communication. The core component is a digital micro controller which performs the basic algorithms necessary for the data acquisition synchronization and the filtering. As a first application, the system was installed in a rotor blade of a wind power turbine in order to monitor the Eigen modes over a longer period of time. Currently the sensor nodes are battery powered.

Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces.

PubMed

Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I; Shenoy, Krishna V; Boahen, Kwabena

2013-06-01

Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system's robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

Protective Controller against Cascade Outages with Selective Harmonic Compensation Function

NASA Astrophysics Data System (ADS)

Abramovich, B. N.; Kuznetsov, P. A.; Sychev, Yu A.

2018-05-01

The paper presents data on the power quality and development of protective devices for the power networks with distributed generation (DG).The research has shown that power quality requirements for DG networks differ from conventional ones. That is why main tendencies, protective equipment and filters should be modified. There isa developed algorithm for detection and prevention of cascade outages that can lead to the blackoutin DG networks and there was a proposed structural scheme for a new active power filter for selective harmonics compensation. Analysis of these theories and equipment led to the development of protective device that could monitor power balance and cut off non-important consumers. The last part of the article describes a microcontroller prototype developed for connection to the existing power station control center.

Experimental study of a self-powered and sensing MR-damper-based vibration control system

NASA Astrophysics Data System (ADS)

Sapiński, Bogdan

2011-10-01

The paper deals with a semi-active vibration control system based on a magnetorheological (MR) damper. The study outlines the model and the structure of the system, and describes its experimental investigation. The conceptual design of this system involves harvesting energy from structural vibrations using an energy extractor based on an electromagnetic transduction mechanism (Faraday's law). The system consists of an electromagnetic induction device (EMI) prototype and an MR damper of RD-1005 series manufactured by Lord Corporation. The energy extracted is applied to control the damping characteristics of the MR damper. The model of the system was used to prove that the proposed vibration control system is feasible. The system was realized in the semi-active control strategy with energy recovery and examined through experiments in the cases where the control coil of the MR damper was voltage-supplied directly from the EMI or voltage-supplied via the rectifier, or supplied with a current control system with two feedback loops. The external loop used the sky-hook algorithm whilst the internal loop used the algorithm switching the photorelay, at the output from the rectifier. Experimental results of the proposed vibration control system were compared with those obtained for the passive system (MR damper is off-state) and for the system with an external power source (conventional system) when the control coil of the MR damper was supplied by a DC power supply and analogue voltage amplifier or a DC power supply and a photorelay. It was demonstrated that the system is able to power-supply the MR damper and can adjust itself to structural vibrations. It was also found that, since the signal of induced voltage from the EMI agrees well with that of the relative velocity signal across the damper, the device can act as a 'velocity-sign' sensor.

Experimental demonstration of using divergence cost-function in SPGD algorithm for coherent beam combining with tip/tilt control.

PubMed

Geng, Chao; Luo, Wen; Tan, Yi; Liu, Hongmei; Mu, Jinbo; Li, Xinyang

2013-10-21

A novel approach of tip/tilt control by using divergence cost function in stochastic parallel gradient descent (SPGD) algorithm for coherent beam combining (CBC) is proposed and demonstrated experimentally in a seven-channel 2-W fiber amplifier array with both phase-locking and tip/tilt control, for the first time to our best knowledge. Compared with the conventional power-in-the-bucket (PIB) cost function for SPGD optimization, the tip/tilt control using divergence cost function ensures wider correction range, automatic switching control of program, and freedom of camera's intensity-saturation. Homemade piezoelectric-ring phase-modulator (PZT PM) and adaptive fiber-optics collimator (AFOC) are developed to correct piston- and tip/tilt-type aberrations, respectively. The PIB cost function is employed for phase-locking via maximization of SPGD optimization, while the divergence cost function is used for tip/tilt control via minimization. An average of 432-μrad of divergence metrics in open loop has decreased to 89-μrad when tip/tilt control implemented. In CBC, the power in the full width at half maximum (FWHM) of the main lobe increases by 32 times, and the phase residual error is less than λ/15.

Digitally Controlled Slot Coupled Patch Array

NASA Technical Reports Server (NTRS)

D'Arista, Thomas; Pauly, Jerry

2010-01-01

A four-element array conformed to a singly curved conducting surface has been demonstrated to provide 2 dB axial ratio of 14 percent, while maintaining VSWR (voltage standing wave ratio) of 2:1 and gain of 13 dBiC. The array is digitally controlled and can be scanned with the LMS Adaptive Algorithm using the power spectrum as the objective, as well as the Direction of Arrival (DoA) of the beam to set the amplitude of the power spectrum. The total height of the array above the conducting surface is 1.5 inches (3.8 cm). A uniquely configured microstrip-coupled aperture over a conducting surface produced supergain characteristics, achieving 12.5 dBiC across the 2-to-2.13- GHz and 2.2-to-2.3-GHz frequency bands. This design is optimized to retain VSWR and axial ratio across the band as well. The four elements are uniquely configured with respect to one another for performance enhancement, and the appropriate phase excitation to each element for scan can be found either by analytical beam synthesis using the genetic algorithm with the measured or simulated far field radiation pattern, or an adaptive algorithm implemented with the digitized signal. The commercially available tuners and field-programmable gate array (FPGA) boards utilized required precise phase coherent configuration control, and with custom code developed by Nokomis, Inc., were shown to be fully functional in a two-channel configuration controlled by FPGA boards. A four-channel tuner configuration and oscilloscope configuration were also demonstrated although algorithm post-processing was required.

Research of high power and stable laser in portable Raman spectrometer based on SHINERS technology

NASA Astrophysics Data System (ADS)

Cui, Yongsheng; Yin, Yu; Wu, Yulin; Ni, Xuxiang; Zhang, Xiuda; Yan, Huimin

2013-08-01

The intensity of Raman light is very weak, which is only from 10-12 to 10-6 of the incident light. In order to obtain the required sensitivity, the traditional Raman spectrometer tends to be heavy weight and large volume, so it is often used as indoor test device. Based on the Shell-Isolated Nanoparticle-Enhanced Raman Spectroscopy (SHINERS) method, Raman optical spectrum signal can be enhanced significantly and the portable Raman spectrometer combined with SHINERS method will be widely used in various fields. The laser source must be stable enough and able to output monochromatic narrow band laser with stable power in the portable Raman spectrometer based on the SHINERS method. When the laser is working, the change of temperature can induce wavelength drift, thus the power stability of excitation light will be affected, so we need to strictly control the working temperature of the laser, In order to ensure the stability of laser power and output current, this paper adopts the WLD3343 laser constant current driver chip of Wavelength Electronics company and MCU P89LPC935 to drive LML - 785.0 BF - XX laser diode(LD). Using this scheme, the Raman spectrometer can be small in size and the drive current can be constant. At the same time, we can achieve functions such as slow start, over-current protection, over-voltage protection, etc. Continuous adjustable output can be realized under control, and the requirement of high power output can be satisfied. Max1968 chip is adopted to realize the accurate control of the laser's temperature. In this way, it can meet the demand of miniaturization. In term of temperature control, integral truncation effect of traditional PID algorithm is big, which is easy to cause static difference. Each output of incremental PID algorithm has nothing to do with the current position, and we can control the output coefficients to avoid full dose output and immoderate adjustment, then the speed of balance will be improved observably. Variable integral incremental digital PID algorithm is used in the TEC temperature control system. The experimental results show that comparing with other schemes, the output power of laser in our scheme is more stable and reliable, moreover the peak value is bigger, and the temperature can be precisely controlled in +/-0.1°C, then the volume of the device is smaller. Using this laser equipment, the ideal Raman spectra of materials can be obtained combined with SHINERS technology and spectrometer equipment.

Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO.

PubMed

Pan, Indranil; Das, Saptarshi

2016-05-01

This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

[An Algorithm to Eliminate Power Frequency Interference in ECG Using Template].

PubMed

Shi, Guohua; Li, Jiang; Xu, Yan; Feng, Liang

2017-01-01

Researching an algorithm to eliminate power frequency interference in ECG. The algorithm first creates power frequency interference template, then, subtracts the template from the original ECG signals, final y, the algorithm gets the ECG signals without interference. Experiment shows the algorithm can eliminate interference effectively and has none side effect to normal signal. It’s efficient and suitable for practice.

Dynamic Power-Saving Method for Wi-Fi Direct Based IoT Networks Considering Variable-Bit-Rate Video Traffic.

PubMed

Jin, Meihua; Jung, Ji-Young; Lee, Jung-Ryun

2016-10-12

With the arrival of the era of Internet of Things (IoT), Wi-Fi Direct is becoming an emerging wireless technology that allows one to communicate through a direct connection between the mobile devices anytime, anywhere. In Wi-Fi Direct-based IoT networks, all devices are categorized by group of owner (GO) and client. Since portability is emphasized in Wi-Fi Direct devices, it is essential to control the energy consumption of a device very efficiently. In order to avoid unnecessary power consumed by GO, Wi-Fi Direct standard defines two power-saving methods: Opportunistic and Notice of Absence (NoA) power-saving methods. In this paper, we suggest an algorithm to enhance the energy efficiency of Wi-Fi Direct power-saving, considering the characteristics of multimedia video traffic. Proposed algorithm utilizes the statistical distribution for the size of video frames and adjusts the lengths of awake intervals in a beacon interval dynamically. In addition, considering the inter-dependency among video frames, the proposed algorithm ensures that a video frame having high priority is transmitted with higher probability than other frames having low priority. Simulation results show that the proposed method outperforms the traditional NoA method in terms of average delay and energy efficiency.

Dynamic Power-Saving Method for Wi-Fi Direct Based IoT Networks Considering Variable-Bit-Rate Video Traffic

PubMed Central

Jin, Meihua; Jung, Ji-Young; Lee, Jung-Ryun

2016-01-01

With the arrival of the era of Internet of Things (IoT), Wi-Fi Direct is becoming an emerging wireless technology that allows one to communicate through a direct connection between the mobile devices anytime, anywhere. In Wi-Fi Direct-based IoT networks, all devices are categorized by group of owner (GO) and client. Since portability is emphasized in Wi-Fi Direct devices, it is essential to control the energy consumption of a device very efficiently. In order to avoid unnecessary power consumed by GO, Wi-Fi Direct standard defines two power-saving methods: Opportunistic and Notice of Absence (NoA) power-saving methods. In this paper, we suggest an algorithm to enhance the energy efficiency of Wi-Fi Direct power-saving, considering the characteristics of multimedia video traffic. Proposed algorithm utilizes the statistical distribution for the size of video frames and adjusts the lengths of awake intervals in a beacon interval dynamically. In addition, considering the inter-dependency among video frames, the proposed algorithm ensures that a video frame having high priority is transmitted with higher probability than other frames having low priority. Simulation results show that the proposed method outperforms the traditional NoA method in terms of average delay and energy efficiency. PMID:27754315

Graphical models for optimal power flow

DOE PAGES

Dvijotham, Krishnamurthy; Chertkov, Michael; Van Hentenryck, Pascal; ...

2016-09-13

Optimal power flow (OPF) is the central optimization problem in electric power grids. Although solved routinely in the course of power grid operations, it is known to be strongly NP-hard in general, and weakly NP-hard over tree networks. In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables are low-dimensional vectors. We adapt the standard dynamic programming algorithm for inference over a tree-structured graphical model to the OPF problem. Combining this with an interval discretization of the nodal variables, we develop an approximation algorithmmore » for the OPF problem. Further, we use techniques from constraint programming (CP) to perform interval computations and adaptive bound propagation to obtain practically efficient algorithms. Compared to previous algorithms that solve OPF with optimality guarantees using convex relaxations, our approach is able to work for arbitrary tree-structured distribution networks and handle mixed-integer optimization problems. Further, it can be implemented in a distributed message-passing fashion that is scalable and is suitable for “smart grid” applications like control of distributed energy resources. In conclusion, numerical evaluations on several benchmark networks show that practical OPF problems can be solved effectively using this approach.« less

A Network Selection Algorithm Considering Power Consumption in Hybrid Wireless Networks

NASA Astrophysics Data System (ADS)

Joe, Inwhee; Kim, Won-Tae; Hong, Seokjoon

In this paper, we propose a novel network selection algorithm considering power consumption in hybrid wireless networks for vertical handover. CDMA, WiBro, WLAN networks are candidate networks for this selection algorithm. This algorithm is composed of the power consumption prediction algorithm and the final network selection algorithm. The power consumption prediction algorithm estimates the expected lifetime of the mobile station based on the current battery level, traffic class and power consumption for each network interface card of the mobile station. If the expected lifetime of the mobile station in a certain network is not long enough compared the handover delay, this particular network will be removed from the candidate network list, thereby preventing unnecessary handovers in the preprocessing procedure. On the other hand, the final network selection algorithm consists of AHP (Analytic Hierarchical Process) and GRA (Grey Relational Analysis). The global factors of the network selection structure are QoS, cost and lifetime. If user preference is lifetime, our selection algorithm selects the network that offers longest service duration due to low power consumption. Also, we conduct some simulations using the OPNET simulation tool. The simulation results show that the proposed algorithm provides longer lifetime in the hybrid wireless network environment.

Adaptive control paradigm for photovoltaic and solid oxide fuel cell in a grid-integrated hybrid renewable energy system.

PubMed

Mumtaz, Sidra; Khan, Laiq

2017-01-01

The hybrid power system (HPS) is an emerging power generation scheme due to the plentiful availability of renewable energy sources. Renewable energy sources are characterized as highly intermittent in nature due to meteorological conditions, while the domestic load also behaves in a quite uncertain manner. In this scenario, to maintain the balance between generation and load, the development of an intelligent and adaptive control algorithm has preoccupied power engineers and researchers. This paper proposes a Hermite wavelet embedded NeuroFuzzy indirect adaptive MPPT (maximum power point tracking) control of photovoltaic (PV) systems to extract maximum power and a Hermite wavelet incorporated NeuroFuzzy indirect adaptive control of Solid Oxide Fuel Cells (SOFC) to obtain a swift response in a grid-connected hybrid power system. A comprehensive simulation testbed for a grid-connected hybrid power system (wind turbine, PV cells, SOFC, electrolyzer, battery storage system, supercapacitor (SC), micro-turbine (MT) and domestic load) is developed in Matlab/Simulink. The robustness and superiority of the proposed indirect adaptive control paradigm are evaluated through simulation results in a grid-connected hybrid power system testbed by comparison with a conventional PI (proportional and integral) control system. The simulation results verify the effectiveness of the proposed control paradigm.

Adaptive control paradigm for photovoltaic and solid oxide fuel cell in a grid-integrated hybrid renewable energy system

PubMed Central

Khan, Laiq

2017-01-01

The hybrid power system (HPS) is an emerging power generation scheme due to the plentiful availability of renewable energy sources. Renewable energy sources are characterized as highly intermittent in nature due to meteorological conditions, while the domestic load also behaves in a quite uncertain manner. In this scenario, to maintain the balance between generation and load, the development of an intelligent and adaptive control algorithm has preoccupied power engineers and researchers. This paper proposes a Hermite wavelet embedded NeuroFuzzy indirect adaptive MPPT (maximum power point tracking) control of photovoltaic (PV) systems to extract maximum power and a Hermite wavelet incorporated NeuroFuzzy indirect adaptive control of Solid Oxide Fuel Cells (SOFC) to obtain a swift response in a grid-connected hybrid power system. A comprehensive simulation testbed for a grid-connected hybrid power system (wind turbine, PV cells, SOFC, electrolyzer, battery storage system, supercapacitor (SC), micro-turbine (MT) and domestic load) is developed in Matlab/Simulink. The robustness and superiority of the proposed indirect adaptive control paradigm are evaluated through simulation results in a grid-connected hybrid power system testbed by comparison with a conventional PI (proportional and integral) control system. The simulation results verify the effectiveness of the proposed control paradigm. PMID:28329015

Computer controllable synchronous shifting of an automatic transmission

DOEpatents

Davis, R.I.; Patil, P.B.

1989-08-08

A multiple forward speed automatic transmission produces its lowest forward speed ratio when a hydraulic clutch and hydraulic brake are disengaged and a one-way clutch connects a ring gear to the transmission casing. Second forward speed ratio results when the hydraulic clutch is engaged to connect the ring gear to the planetary carrier of a second gear set. Reverse drive and regenerative operation result when an hydraulic brake fixes the planetary and the direction of power flow is reversed. Various sensors produce signals representing the torque at the output of the transmission or drive wheels, the speed of the power source, and the hydraulic pressure applied to a clutch and brake. A control algorithm produces input data representing a commanded upshift, a commanded downshift, a commanded transmission output torque, and commanded power source speed. A microprocessor processes the inputs and produces a response to them in accordance with the execution of a control algorithm. Output or response signals cause selective engagement and disengagement of the clutch and brake at a rate that satisfies the requirements for a short gear ratio change and smooth torque transfer between the friction elements. 6 figs.

New sensorless, efficient optimized and stabilized v/f control for pmsm machines

NASA Astrophysics Data System (ADS)

Jafari, Seyed Hesam

With the rapid advances in power electronics and motor drive technologies in recent decades, permanent magnet synchronous machines (PMSM) have found extensive applications in a variety of industrial systems due to its many desirable features such as high power density, high efficiency, and high torque to current ratio, low noise, and robustness. In low dynamic applications like pumps, fans and compressors where the motor speed is nearly constant, usage of a simple control algorithm that can be implemented with least number of the costly external hardware can be highly desirable for industry. In recent published works, for low power PMSMs, a new sensorless volts-per-hertz (V/f) controlling method has been proposed which can be used for PMSM drive applications where the motor speed is constant. Moreover, to minimize the cost of motor implementation, the expensive rotor damper winding was eliminated. By removing the damper winding, however, instability problems normally occur inside of the motor which in some cases can be harmful for a PMSM drive. As a result, to address the instability issue, a stabilizing loop was developed and added to the conventional V/f. By further studying the proposed sensorless stabilized V/f, and calculating power loss, it became known that overall motor efficiency still is needed to be improved and optimized. This thesis suggests a new V/f control method for PMSMs, where both efficiency and stability problems are addressed. Also, although in nearly all recent related research, methods have been applied to low power PMSM, for the first time, in this thesis, the suggested method is implemented for a medium power 15 kW PMSM. A C2000 F2833x Digital Signal Processor (DSP) is used as controller part for the student custom built PMSM drive, but instead of programming the DSP in Assembly or C, the main control algorithm was developed in a rapid prototype software environment which here Matlab Simulink embedded code library is used.

Dynamic Modeling and Grid Interaction of a Tidal and River Generator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Muljadi, Eduard; Gevorgian, Vahan; Donegan, James

This presentation provides a high-level overview of the deployment of a river generator installed in a small system. The turbine dynamics of a river generator, electrical generator, and power converter are modeled in detail. Various simulations can be exercised, and the impact of different control algorithms, failures of power switches, and corresponding impacts can be examined.

Advanced communications technology satellite high burst rate link evaluation terminal power control and rain fade software test plan, version 1.0

NASA Technical Reports Server (NTRS)

Reinhart, Richard C.

1993-01-01

The Power Control and Rain Fade Software was developed at the NASA Lewis Research Center to support the Advanced Communications Technology Satellite High Burst Rate Link Evaluation Terminal (ACTS HBR-LET). The HBR-LET is an experimenters terminal to communicate with the ACTS for various experiments by government, university, and industry agencies. The Power Control and Rain Fade Software is one segment of the Control and Performance Monitor (C&PM) Software system of the HBR-LET. The Power Control and Rain Fade Software automatically controls the LET uplink power to compensate for signal fades. Besides power augmentation, the C&PM Software system is also responsible for instrument control during HBR-LET experiments, control of the Intermediate Frequency Switch Matrix on board the ACTS to yield a desired path through the spacecraft payload, and data display. The Power Control and Rain Fade Software User's Guide, Version 1.0 outlines the commands and procedures to install and operate the Power Control and Rain Fade Software. The Power Control and Rain Fade Software Maintenance Manual, Version 1.0 is a programmer's guide to the Power Control and Rain Fade Software. This manual details the current implementation of the software from a technical perspective. Included is an overview of the Power Control and Rain Fade Software, computer algorithms, format representations, and computer hardware configuration. The Power Control and Rain Fade Test Plan provides a step-by-step procedure to verify the operation of the software using a predetermined signal fade event. The Test Plan also provides a means to demonstrate the capability of the software.

Optimal reactive power planning for distribution systems considering intermittent wind power using Markov model and genetic algorithm

NASA Astrophysics Data System (ADS)

Li, Cheng

Wind farms, photovoltaic arrays, fuel cells, and micro-turbines are all considered to be Distributed Generation (DG). DG is defined as the generation of power which is dispersed throughout a utility's service territory and either connected to the utility's distribution system or isolated in a small grid. This thesis addresses modeling and economic issues pertaining to the optimal reactive power planning for distribution system with wind power generation (WPG) units. Wind farms are inclined to cause reverse power flows and voltage variations due to the random-like outputs of wind turbines. To deal with this kind of problem caused by wide spread usage of wind power generation, this thesis investigates voltage and reactive power controls in such a distribution system. Consequently static capacitors (SC) and transformer taps are introduced into the system and treated as controllers. For the purpose of getting optimum voltage and realizing reactive power control, the research proposes a proper coordination among the controllers like on-load tap changer (OLTC), feeder-switched capacitors. What's more, in order to simulate its uncertainty, the wind power generation is modeled by the Markov model. In that way, calculating the probabilities for all the scenarios is possible. Some outputs with consecutive and discrete values have been used for transition between successive time states and within state wind speeds. The thesis will describe the method to generate the wind speed time series from the transition probability matrix. After that, utilizing genetic algorithm, the optimal locations of SCs, the sizes of SCs and transformer taps are determined so as to minimize the cost or minimize the power loss, and more importantly improve voltage profiles. The applicability of the proposed method is verified through simulation on a 9-bus system and a 30-bus system respectively. At last, the simulation results indicate that as long as the available capacitors are able to sufficiently compensate the reactive power demand, the DG operation no longer imposes a significant effect on the voltage fluctuations in the distribution system. And the proposed approach is efficient, simple and straightforward.

A wirelessly-powered homecage with animal behavior analysis and closed-loop power control.

PubMed

Yaoyao Jia; Zheyuan Wang; Canales, Daniel; Tinkler, Morgan; Chia-Chun Hsu; Madsen, Teresa E; Mirbozorgi, S Abdollah; Rainnie, Donald; Ghovanloo, Maysam

2016-08-01

This paper presents a new EnerCage-homecage system, EnerCage-HC2, for longitudinal electrophysiology data acquisition experiments on small freely moving animal subjects, such as rodents. EnerCage-HC2 is equipped with multi-coil wireless power transmission (WPT), closed-loop power control, bidirectional data communication via Bluetooth Low Energy (BLE), and Microsoft Kinect® based animal behavior tracking and analysis. The EnerCage-HC2 achieves a homogeneous power transfer efficiency (PTE) of 14% on average, with ~42 mW power delivered to the load (PDL) at a nominal height of 7 cm by the closed-loop power control mechanism. The Microsoft Kinect® behavioral analysis algorithm can not only track the animal position in real-time but also classify 5 different types of rodent behaviors: standstill, walking, grooming, rearing, and rotating. A proof-of-concept in vivo experiment was conducted on two awake freely behaving rats while successfully operating a one-channel stimulator and generating an ethogram.

Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control

NASA Astrophysics Data System (ADS)

Othman, Ahmed M.; El-arini, Mahdi M. M.; Ghitas, Ahmed; Fathy, Ahmed

2012-12-01

In the recent years, the solar energy becomes one of the most important alternative sources of electric energy, so it is important to improve the efficiency and reliability of the photovoltaic (PV) systems. Maximum power point tracking (MPPT) plays an important role in photovoltaic power systems because it maximize the power output from a PV system for a given set of conditions, and therefore maximize their array efficiency. This paper presents a maximum power point tracker (MPPT) using Fuzzy Logic theory for a PV system. The work is focused on the well known Perturb and Observe (P&O) algorithm and is compared to a designed fuzzy logic controller (FLC). The simulation work dealing with MPPT controller; a DC/DC Ćuk converter feeding a load is achieved. The results showed that the proposed Fuzzy Logic MPPT in the PV system is valid.

High-frequency AC/DC converter with unity power factor and minimum harmonic distortion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wernekinch, E.R.

1987-01-01

The power factor is controlled by adjusting the relative position of the fundamental component of an optimized PWM-type voltage with respect to the supply voltage. Current harmonic distortion is minimized by the use of optimized firing angles for the converter at a frequency where GTO's can be used. This feature makes this approach very attractive at power levels of 100 to 600 kW. To obtain the optimized PWM pattern, a steepest descent digital computer algorithm is used. Digital-computer simulations are performed and a low-power model is constructed and tested to verify the concepts and the behavior of the model. Experimentalmore » results show that unity power factor is achieved and that the distortion in the phase currents is 10.4% at 90% of full load. This is less than achievable with sinusoidal PWM, harmonic elimination, hysteresis control, and deadbeat control for the same switching frequency.« less

Deep Learning-Based Data Forgery Detection in Automatic Generation Control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Fengli; Li, Qinghua

Automatic Generation Control (AGC) is a key control system in the power grid. It is used to calculate the Area Control Error (ACE) based on frequency and tie-line power flow between balancing areas, and then adjust power generation to maintain the power system frequency in an acceptable range. However, attackers might inject malicious frequency or tie-line power flow measurements to mislead AGC to do false generation correction which will harm the power grid operation. Such attacks are hard to be detected since they do not violate physical power system models. In this work, we propose algorithms based on Neural Networkmore » and Fourier Transform to detect data forgery attacks in AGC. Different from the few previous work that rely on accurate load prediction to detect data forgery, our solution only uses the ACE data already available in existing AGC systems. In particular, our solution learns the normal patterns of ACE time series and detects abnormal patterns caused by artificial attacks. Evaluations on the real ACE dataset show that our methods have high detection accuracy.« less

Energy Efficiency Maximization for WSNs with Simultaneous Wireless Information and Power Transfer

PubMed Central

Yu, Hongyan; Zhang, Yongqiang; Yang, Yuanyuan; Ji, Luyue

2017-01-01

Recently, the simultaneous wireless information and power transfer (SWIPT) technique has been regarded as a promising approach to enhance performance of wireless sensor networks with limited energy supply. However, from a green communication perspective, energy efficiency optimization for SWIPT system design has not been investigated in Wireless Rechargeable Sensor Networks (WRSNs). In this paper, we consider the tradeoffs between energy efficiency and three factors including spectral efficiency, the transmit power and outage target rate for two different modes, i.e., power splitting (PS) and time switching modes (TS), at the receiver. Moreover, we formulate the energy efficiency maximization problem subject to the constraints of minimum Quality of Service (QoS), minimum harvested energy and maximum transmission power as non-convex optimization problem. In particular, we focus on optimizing power control and power allocation policy in PS and TS modes to maximize energy efficiency of data transmission. For PS and TS modes, we propose the corresponding algorithm to characterize a non-convex optimization problem that takes into account the circuit power consumption and the harvested energy. By exploiting nonlinear fractional programming and Lagrangian dual decomposition, we propose suboptimal iterative algorithms to obtain the solutions of non-convex optimization problems. Furthermore, we derive the outage probability and effective throughput from the scenarios that the transmitter does not or partially know the channel state information (CSI) of the receiver. Simulation results illustrate that the proposed optimal iterative algorithm can achieve optimal solutions within a small number of iterations and various tradeoffs between energy efficiency and spectral efficiency, transmit power and outage target rate, respectively. PMID:28820496

Energy Efficiency Maximization for WSNs with Simultaneous Wireless Information and Power Transfer.

PubMed

Yu, Hongyan; Zhang, Yongqiang; Guo, Songtao; Yang, Yuanyuan; Ji, Luyue

2017-08-18

Recently, the simultaneous wireless information and power transfer (SWIPT) technique has been regarded as a promising approach to enhance performance of wireless sensor networks with limited energy supply. However, from a green communication perspective, energy efficiency optimization for SWIPT system design has not been investigated in Wireless Rechargeable Sensor Networks (WRSNs). In this paper, we consider the tradeoffs between energy efficiency and three factors including spectral efficiency, the transmit power and outage target rate for two different modes, i.e., power splitting (PS) and time switching modes (TS), at the receiver. Moreover, we formulate the energy efficiency maximization problem subject to the constraints of minimum Quality of Service (QoS), minimum harvested energy and maximum transmission power as non-convex optimization problem. In particular, we focus on optimizing power control and power allocation policy in PS and TS modes to maximize energy efficiency of data transmission. For PS and TS modes, we propose the corresponding algorithm to characterize a non-convex optimization problem that takes into account the circuit power consumption and the harvested energy. By exploiting nonlinear fractional programming and Lagrangian dual decomposition, we propose suboptimal iterative algorithms to obtain the solutions of non-convex optimization problems. Furthermore, we derive the outage probability and effective throughput from the scenarios that the transmitter does not or partially know the channel state information (CSI) of the receiver. Simulation results illustrate that the proposed optimal iterative algorithm can achieve optimal solutions within a small number of iterations and various tradeoffs between energy efficiency and spectral efficiency, transmit power and outage target rate, respectively.

Real-Time Control of an Ensemble of Heterogeneous Resources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bernstein, Andrey; Bouman, Niek J.; Le Boudec, Jean-Yves

This paper focuses on the problem of controlling an ensemble of heterogeneous resources connected to an electrical grid at the same point of common coupling (PCC). The controller receives an aggregate power setpoint for the ensemble in real time and tracks this setpoint by issuing individual optimal setpoints to the resources. The resources can have continuous or discrete nature (e.g., heating systems consisting of a finite number of heaters that each can be either switched on or off) and/or can be highly uncertain (e.g., photovoltaic (PV) systems or residential loads). A naive approach would lead to a stochastic mixed-integer optimizationmore » problem to be solved at the controller at each time step, which might be infeasible in real time. Instead, we allow the controller to solve a continuous convex optimization problem and compensate for the errors at the resource level by using a variant of the well-known error diffusion algorithm. We give conditions guaranteeing that our algorithm tracks the power setpoint at the PCC on average while issuing optimal setpoints to individual resources. We illustrate the approach numerically by controlling a collection of batteries, PV systems, and discrete loads.« less

The New Feedback Control System of RFX-mod Based on the MARTe Real-Time Framework

NASA Astrophysics Data System (ADS)

Manduchi, G.; Luchetta, A.; Soppelsa, A.; Taliercio, C.

2014-06-01

A real-time system has been successfully used since 2004 in the RFX-mod nuclear fusion experiment to control the position of the plasma and its Magneto Hydrodynamic (MHD) modes. However, its latency and the limited computation power of the used processors prevented the usage of more aggressive control algorithms. Therefore a new hardware and software architecture has been designed to overcome such limitations and to provide a shorter latency and a much increased computation power. The new system is based on a Linux multi-core server and uses MARTe, a framework for real-time control which is gaining interest in the fusion community.

Tracking Control of Shape-Memory-Alloy Actuators Based on Self-Sensing Feedback and Inverse Hysteresis Compensation

PubMed Central

Liu, Shu-Hung; Huang, Tse-Shih; Yen, Jia-Yush

2010-01-01

Shape memory alloys (SMAs) offer a high power-to-weight ratio, large recovery strain, and low driving voltages, and have thus attracted considerable research attention. The difficulty of controlling SMA actuators arises from their highly nonlinear hysteresis and temperature dependence. This paper describes a combination of self-sensing and model-based control, where the model includes both the major and minor hysteresis loops as well as the thermodynamics effects. The self-sensing algorithm uses only the power width modulation (PWM) signal and requires no heavy equipment. The method can achieve high-accuracy servo control and is especially suitable for miniaturized applications. PMID:22315530

Optimal configuration of power grid sources based on optimal particle swarm algorithm

NASA Astrophysics Data System (ADS)

Wen, Yuanhua

2018-04-01

In order to optimize the distribution problem of power grid sources, an optimized particle swarm optimization algorithm is proposed. First, the concept of multi-objective optimization and the Pareto solution set are enumerated. Then, the performance of the classical genetic algorithm, the classical particle swarm optimization algorithm and the improved particle swarm optimization algorithm are analyzed. The three algorithms are simulated respectively. Compared with the test results of each algorithm, the superiority of the algorithm in convergence and optimization performance is proved, which lays the foundation for subsequent micro-grid power optimization configuration solution.

Flywheel Charge/Discharge Control Developed

NASA Technical Reports Server (NTRS)

Beach, Raymond.F.; Kenny, Barbara H.

2001-01-01

A control algorithm developed at the NASA Glenn Research Center will allow a flywheel energy storage system to interface with the electrical bus of a space power system. The controller allows the flywheel to operate in both charge and discharge modes. Charge mode is used to store additional energy generated by the solar arrays on the spacecraft during insolation. During charge mode, the flywheel spins up to store the additional electrical energy as rotational mechanical energy. Discharge mode is used during eclipse when the flywheel provides the power to the spacecraft. During discharge mode, the flywheel spins down to release the stored rotational energy.

Control of microfabricated structures powered by flagellated bacteria using phototaxis

NASA Astrophysics Data System (ADS)

Steager, Edward; Kim, Chang-Beom; Patel, Jigarkumar; Bith, Socheth; Naik, Chandan; Reber, Lindsay; Kim, Min Jun

2007-06-01

Flagellated bacteria have been employed as microactuators in low Reynolds number fluidic environments. SU-8 microstructures have been fabricated and released on the surface of swarming Serratia marcescens, and the flagella propel the structures along the swarm surface. Phototactic control of these structures is demonstrated by exposing the localized regions of the swarm to ultraviolet light. The authors additionally discuss the control of microstructures in an open channel powered by bacteria which have been docked through a blotting technique. A tracking algorithm has been developed to analyze swarming patterns of the bacteria as well as the kinematics of the microstructures.

Developing of method for primary frequency control droop and deadband actual values estimation

NASA Astrophysics Data System (ADS)

Nikiforov, A. A.; Chaplin, A. G.

2017-11-01

Operation of thermal power plant generation equipment, which participates in standardized primary frequency control (SPFC), must meet specific requirements. These requirements are formalized as nine algorithmic criteria, which are used for automatic monitoring of power plant participation in SPFC. One of these criteria - primary frequency control droop and deadband actual values estimation is considered in detail in this report. Experience shows that existing estimation method sometimes doesn’t work properly. Author offers alternative method, which allows estimating droop and deadband actual values more accurately. This method was implemented as a software application.

SPHERES Facility

NASA Technical Reports Server (NTRS)

Martinez, Andres; Benavides, Jose Victor; Ormsby, Steve L.; GuarnerosLuna, Ali

2014-01-01

Synchronized Position Hold, Engage, Reorient, Experimental Satellites (SPHERES) are bowling-ball sized satellites that provide a test bed for development and research into multi-body formation flying, multi-spacecraft control algorithms, and free-flying physical and material science investigations. Up to three self-contained free-flying satellites can fly within the cabin of the International Space Station (ISS), performing flight formations, testing of control algorithms or as a platform for investigations requiring this unique free-flying test environment. Each satellite is a self-contained unit with power, propulsion, computers, navigation equipment, and provides physical and electrical connections (via standardized expansion ports) for Principal Investigator (PI) provided hardware and sensors.

Description of real-time Ada software implementation of a power system monitor for the Space Station Freedom PMAD DC testbed

NASA Technical Reports Server (NTRS)

Ludwig, Kimberly; Mackin, Michael; Wright, Theodore

1991-01-01

The authors describe the Ada language software developed to perform the electrical power system monitoring functions for the NASA Lewis Research Center's Power Management and Distribution (PMAD) DC testbed. The results of the effort to implement this monitor are presented. The PMAD DC testbed is a reduced-scale prototype of the electric power system to be used in Space Station Freedom. The power is controlled by smart switches known as power control components (or switchgear). The power control components are currently coordinated by five Compaq 386/20e computers connected through an 802.4 local area network. The power system monitor algorithm comprises several functions, including periodic data acquisition, data smoothing, system performance analysis, and status reporting. Data are collected from the switchgear sensors every 100 ms, then passed through a 2-Hz digital filter. System performance analysis includes power interruption and overcurrent detection. The system monitor required a hardware timer interrupt to activate the data acquisition function. The execution time of the code was optimized by using an assembly language routine. The routine allows direct vectoring of the processor to Ada language procedures that perform periodic control activities.

TLBO based Voltage Stable Environment Friendly Economic Dispatch Considering Real and Reactive Power Constraints

NASA Astrophysics Data System (ADS)

Verma, H. K.; Mafidar, P.

2013-09-01

In view of growing concern towards environment, power system engineers are forced to generate quality green energy. Hence the economic dispatch (ED) aims at the power generation to meet the load demand at minimum fuel cost with environmental and voltage constraints along with essential constraints on real and reactive power. The emission control which reduces the negative impact on environment is achieved by including the additional constraints in ED problem. Presently, the power system mostly operates near its stability limits, therefore with increased demand the system faces voltage problem. The bus voltages are brought within limit in the present work by placement of static var compensator (SVC) at weak bus which is identified from bus participation factor. The optimal size of SVC is determined by univariate search method. This paper presents the use of Teaching Learning based Optimization (TLBO) algorithm for voltage stable environment friendly ED problem with real and reactive power constraints. The computational effectiveness of TLBO is established through test results over particle swarm optimization (PSO) and Big Bang-Big Crunch (BB-BC) algorithms for the ED problem.

Sub-synchronous resonance damping using high penetration PV plant

NASA Astrophysics Data System (ADS)

Khayyatzadeh, M.; Kazemzadeh, R.

2017-02-01

The growing need to the clean and renewable energy has led to the fast development of transmission voltage-level photovoltaic (PV) plants all over the world. These large scale PV plants are going to be connected to power systems and one of the important subjects that should be investigated is the impact of these plants on the power system stability. Can large scale PV plants help to damp sub-synchronous resonance (SSR) and how? In this paper, this capability of a large scale PV plant is investigated. The IEEE Second Benchmark Model aggregated with a PV plant is utilized as the case study. A Wide Area Measurement System (WAMS) based conventional damping controller is designed and added to the main control loop of PV plant in order to damp the SSR and also investigation of the destructive effect of time delay in remote feedback signal. A new optimization algorithm called teaching-learning-based-optimization (TLBO) algorithm has been used for managing the optimization problems. Fast Furrier Transformer (FFT) analysis and also transient simulations of detailed nonlinear system are considered to investigate the performance of the controller. Robustness of the proposed system has been analyzed by facing the system with disturbances leading to significant changes in generator and power system operating point, fault duration time and PV plant generated power. All the simulations are carried out in MATLAB/SIMULINK environment.

NREL`s variable speed test bed: Preliminary results

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carlin, P.W.; Fingersh, L.J.; Fuchs, E.F.

1996-10-01

Under an NREL subcontract, the Electrical and Computer Engineering Department of the University of Colorado (CU) designed a 20-kilowatt, 12-pole, permanent-magnet, electric generator and associated custom power electronics modules. This system can supply power over a generator speed range from 60 to 120 RPM. The generator was fabricated and assembled by the Denver electric-motor manufacturer, Unique Mobility, and the power electronics modules were designed and fabricated at the University. The generator was installed on a 56-foot tower in the modified nacelle of a Grumman Windstream 33 wind turbine in early October 1995. For checkout it was immediately loaded directly intomore » a three-phase resistive load in which it produced 3.5 kilowatts of power. Abstract only included. The ten-meter Grumman host wind machine is equipped with untwisted, untapered, NREL series S809 blades. The machine was instrumented to record both mechanical hub power and electrical power delivered to the utility. Initial tests are focusing on validating the calculated power surface. This mathematical surface shows the wind machine power as a function of both wind speed and turbine rotor speed. Upon the completion of this task, maximum effort will be directed toward filling a test matrix in which variable-speed operation will be contrasted with constant-speed mode by switching the variable speed control algorithm with the baseline constant speed control algorithm at 10 minutes time intervals. Other quantities in the test matrix will be analyzed to detect variable speed-effects on structural loads and power quality.« less

Intra-Minute Cloud Passing Forecasting Based on a Low Cost IoT Sensor-A Solution for Smoothing the Output Power of PV Power Plants.

PubMed

Sukič, Primož; Štumberger, Gorazd

2017-05-13

Clouds moving at a high speed in front of the Sun can cause step changes in the output power of photovoltaic (PV) power plants, which can lead to voltage fluctuations and stability problems in the connected electricity networks. These effects can be reduced effectively by proper short-term cloud passing forecasting and suitable PV power plant output power control. This paper proposes a low-cost Internet of Things (IoT)-based solution for intra-minute cloud passing forecasting. The hardware consists of a Raspberry PI Model B 3 with a WiFi connection and an OmniVision OV5647 sensor with a mounted wide-angle lens, a circular polarizing (CPL) filter and a natural density (ND) filter. The completely new algorithm for cloud passing forecasting uses the green and blue colors in the photo to determine the position of the Sun, to recognize the clouds, and to predict their movement. The image processing is performed in several stages, considering selectively only a small part of the photo relevant to the movement of the clouds in the vicinity of the Sun in the next minute. The proposed algorithm is compact, fast and suitable for implementation on low cost processors with low computation power. The speed of the cloud parts closest to the Sun is used to predict when the clouds will cover the Sun. WiFi communication is used to transmit this data to the PV power plant control system in order to decrease the output power slowly and smoothly.

Intra-Minute Cloud Passing Forecasting Based on a Low Cost IoT Sensor—A Solution for Smoothing the Output Power of PV Power Plants

PubMed Central

Sukič, Primož; Štumberger, Gorazd

2017-01-01

Clouds moving at a high speed in front of the Sun can cause step changes in the output power of photovoltaic (PV) power plants, which can lead to voltage fluctuations and stability problems in the connected electricity networks. These effects can be reduced effectively by proper short-term cloud passing forecasting and suitable PV power plant output power control. This paper proposes a low-cost Internet of Things (IoT)-based solution for intra-minute cloud passing forecasting. The hardware consists of a Raspberry PI Model B 3 with a WiFi connection and an OmniVision OV5647 sensor with a mounted wide-angle lens, a circular polarizing (CPL) filter and a natural density (ND) filter. The completely new algorithm for cloud passing forecasting uses the green and blue colors in the photo to determine the position of the Sun, to recognize the clouds, and to predict their movement. The image processing is performed in several stages, considering selectively only a small part of the photo relevant to the movement of the clouds in the vicinity of the Sun in the next minute. The proposed algorithm is compact, fast and suitable for implementation on low cost processors with low computation power. The speed of the cloud parts closest to the Sun is used to predict when the clouds will cover the Sun. WiFi communication is used to transmit this data to the PV power plant control system in order to decrease the output power slowly and smoothly. PMID:28505078

Increased Energy Delivery for Parallel Battery Packs with No Regulated Bus

NASA Astrophysics Data System (ADS)

Hsu, Chung-Ti

In this dissertation, a new approach to paralleling different battery types is presented. A method for controlling charging/discharging of different battery packs by using low-cost bi-directional switches instead of DC-DC converters is proposed. The proposed system architecture, algorithms, and control techniques allow batteries with different chemistry, voltage, and SOC to be properly charged and discharged in parallel without causing safety problems. The physical design and cost for the energy management system is substantially reduced. Additionally, specific types of failures in the maximum power point tracking (MPPT) in a photovoltaic (PV) system when tracking only the load current of a DC-DC converter are analyzed. The periodic nonlinear load current will lead MPPT realized by the conventional perturb and observe (P&O) algorithm to be problematic. A modified MPPT algorithm is proposed and it still only requires typically measured signals, yet is suitable for both linear and periodic nonlinear loads. Moreover, for a modular DC-DC converter using several converters in parallel, the input power from PV panels is processed and distributed at the module level. Methods for properly implementing distributed MPPT are studied. A new approach to efficient MPPT under partial shading conditions is presented. The power stage architecture achieves fast input current change rate by combining a current-adjustable converter with a few converters operating at a constant current.

Adaptive power allocation schemes based on IAFS algorithm for OFDM-based cognitive radio systems

NASA Astrophysics Data System (ADS)

Zhang, Shuying; Zhao, Xiaohui; Liang, Cong; Ding, Xu

2017-01-01

In cognitive radio (CR) systems, reasonable power allocation can increase transmission rate of CR users or secondary users (SUs) as much as possible and at the same time insure normal communication among primary users (PUs). This study proposes an optimal power allocation scheme for the OFDM-based CR system with one SU influenced by multiple PU interference constraints. This scheme is based on an improved artificial fish swarm (IAFS) algorithm in combination with the advantage of conventional artificial fish swarm (ASF) algorithm and particle swarm optimisation (PSO) algorithm. In performance comparison of IAFS algorithm with other intelligent algorithms by simulations, the superiority of the IAFS algorithm is illustrated; this superiority results in better performance of our proposed scheme than that of the power allocation algorithms proposed by the previous studies in the same scenario. Furthermore, our proposed scheme can obtain higher transmission data rate under the multiple PU interference constraints and the total power constraint of SU than that of the other mentioned works.

Feedback power control strategies in wireless sensor networks with joint channel decoding.

PubMed

Abrardo, Andrea; Ferrari, Gianluigi; Martalò, Marco; Perna, Fabio

2009-01-01

In this paper, we derive feedback power control strategies for block-faded multiple access schemes with correlated sources and joint channel decoding (JCD). In particular, upon the derivation of the feasible signal-to-noise ratio (SNR) region for the considered multiple access schemes, i.e., the multidimensional SNR region where error-free communications are, in principle, possible, two feedback power control strategies are proposed: (i) a classical feedback power control strategy, which aims at equalizing all link SNRs at the access point (AP), and (ii) an innovative optimized feedback power control strategy, which tries to make the network operational point fall in the feasible SNR region at the lowest overall transmit energy consumption. These strategies will be referred to as "balanced SNR" and "unbalanced SNR," respectively. While they require, in principle, an unlimited power control range at the sources, we also propose practical versions with a limited power control range. We preliminary consider a scenario with orthogonal links and ideal feedback. Then, we analyze the robustness of the proposed power control strategies to possible non-idealities, in terms of residual multiple access interference and noisy feedback channels. Finally, we successfully apply the proposed feedback power control strategies to a limiting case of the class of considered multiple access schemes, namely a central estimating officer (CEO) scenario, where the sensors observe noisy versions of a common binary information sequence and the AP's goal is to estimate this sequence by properly fusing the soft-output information output by the JCD algorithm.

Control algorithm implementation for a redundant degree of freedom manipulator

NASA Technical Reports Server (NTRS)

Cohan, Steve

1991-01-01

This project's purpose is to develop and implement control algorithms for a kinematically redundant robotic manipulator. The manipulator is being developed concurrently by Odetics Inc., under internal research and development funding. This SBIR contract supports algorithm conception, development, and simulation, as well as software implementation and integration with the manipulator hardware. The Odetics Dexterous Manipulator is a lightweight, high strength, modular manipulator being developed for space and commercial applications. It has seven fully active degrees of freedom, is electrically powered, and is fully operational in 1 G. The manipulator consists of five self-contained modules. These modules join via simple quick-disconnect couplings and self-mating connectors which allow rapid assembly/disassembly for reconfiguration, transport, or servicing. Each joint incorporates a unique drive train design which provides zero backlash operation, is insensitive to wear, and is single fault tolerant to motor or servo amplifier failure. The sensing system is also designed to be single fault tolerant. Although the initial prototype is not space qualified, the design is well-suited to meeting space qualification requirements. The control algorithm design approach is to develop a hierarchical system with well defined access and interfaces at each level. The high level endpoint/configuration control algorithm transforms manipulator endpoint position/orientation commands to joint angle commands, providing task space motion. At the same time, the kinematic redundancy is resolved by controlling the configuration (pose) of the manipulator, using several different optimizing criteria. The center level of the hierarchy servos the joints to their commanded trajectories using both linear feedback and model-based nonlinear control techniques. The lowest control level uses sensed joint torque to close torque servo loops, with the goal of improving the manipulator dynamic behavior. The control algorithms are subjected to a dynamic simulation before implementation.

Smart Adaptive Socket to Improve Fit and Relieve Pain in Wounded Warriors

DTIC Science & Technology

2016-10-01

applications were developed for wireless interaction with the socket system firmware. A control algorithm was designed and tested. Clinical trial...interface, Dynamic segmental volume control, Wireless connection, Pressure control system. 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18...charging jack, and power button are included in the design. A Bluetooth 4 radio is also included to allow for advanced user control via smartphone. The

A New Powered Lower Limb Prosthesis Control Framework Based on Adaptive Dynamic Programming.

PubMed

Wen, Yue; Si, Jennie; Gao, Xiang; Huang, Stephanie; Huang, He Helen

2017-09-01

This brief presents a novel application of adaptive dynamic programming (ADP) for optimal adaptive control of powered lower limb prostheses, a type of wearable robots to assist the motor function of the limb amputees. Current control of these robotic devices typically relies on finite state impedance control (FS-IC), which lacks adaptability to the user's physical condition. As a result, joint impedance settings are often customized manually and heuristically in clinics, which greatly hinder the wide use of these advanced medical devices. This simulation study aimed at demonstrating the feasibility of ADP for automatic tuning of the twelve knee joint impedance parameters during a complete gait cycle to achieve balanced walking. Given that the accurate models of human walking dynamics are difficult to obtain, the model-free ADP control algorithms were considered. First, direct heuristic dynamic programming (dHDP) was applied to the control problem, and its performance was evaluated on OpenSim, an often-used dynamic walking simulator. For the comparison purposes, we selected another established ADP algorithm, the neural fitted Q with continuous action (NFQCA). In both cases, the ADP controllers learned to control the right knee joint and achieved balanced walking, but dHDP outperformed NFQCA in this application during a 200 gait cycle-based testing.

Resonance frequency control of RF normal conducting cavity using gradient estimator of reflected power

NASA Astrophysics Data System (ADS)

Leewe, R.; Shahriari, Z.; Moallem, M.

2017-10-01

Control of the natural resonance frequency of an RF cavity is essential for accelerator structures due to their high cavity sensitivity to internal and external vibrations and the dependency of resonant frequency on temperature changes. Due to the relatively high radio frequencies involved (MHz to GHz), direct measurement of the resonant frequency for real-time control is not possible by using conventional microcontroller hardware. So far, all operational cavities are tuned using phase comparison techniques. The temperature dependent phase measurements render this technique labor and time intensive. To eliminate the phase measurement, reduce man hours and speed up cavity start up time, this paper presents a control theme that relies solely on the reflected power measurement. The control algorithm for the nonlinear system is developed through Lyapunov's method. The controller stabilizes the resonance frequency of the cavity using a nonlinear control algorithm in combination with a gradient estimation method. Experimental results of the proposed system on a test cavity show that the resonance frequency can be tuned to its optimum operating point while the start up time of a single cavity and the accompanied man hours are significantly decreased. A test result of the fully commissioned control system on one of TRIUMF's DTL tanks verifies its performance under real environmental conditions.

Dynamic Modeling, Model-Based Control, and Optimization of Solid Oxide Fuel Cells

NASA Astrophysics Data System (ADS)

Spivey, Benjamin James

2011-07-01

Solid oxide fuel cells are a promising option for distributed stationary power generation that offers efficiencies ranging from 50% in stand-alone applications to greater than 80% in cogeneration. To advance SOFC technology for widespread market penetration, the SOFC should demonstrate improved cell lifetime and load-following capability. This work seeks to improve lifetime through dynamic analysis of critical lifetime variables and advanced control algorithms that permit load-following while remaining in a safe operating zone based on stress analysis. Control algorithms typically have addressed SOFC lifetime operability objectives using unconstrained, single-input-single-output control algorithms that minimize thermal transients. Existing SOFC controls research has not considered maximum radial thermal gradients or limits on absolute temperatures in the SOFC. In particular, as stress analysis demonstrates, the minimum cell temperature is the primary thermal stress driver in tubular SOFCs. This dissertation presents a dynamic, quasi-two-dimensional model for a high-temperature tubular SOFC combined with ejector and prereformer models. The model captures dynamics of critical thermal stress drivers and is used as the physical plant for closed-loop control simulations. A constrained, MIMO model predictive control algorithm is developed and applied to control the SOFC. Closed-loop control simulation results demonstrate effective load-following, constraint satisfaction for critical lifetime variables, and disturbance rejection. Nonlinear programming is applied to find the optimal SOFC size and steady-state operating conditions to minimize total system costs.

On-line experimental validation of a model-based diagnostic algorithm dedicated to a solid oxide fuel cell system

NASA Astrophysics Data System (ADS)

Polverino, Pierpaolo; Esposito, Angelo; Pianese, Cesare; Ludwig, Bastian; Iwanschitz, Boris; Mai, Andreas

2016-02-01

In the current energetic scenario, Solid Oxide Fuel Cells (SOFCs) exhibit appealing features which make them suitable for environmental-friendly power production, especially for stationary applications. An example is represented by micro-combined heat and power (μ-CHP) generation units based on SOFC stacks, which are able to produce electric and thermal power with high efficiency and low pollutant and greenhouse gases emissions. However, the main limitations to their diffusion into the mass market consist in high maintenance and production costs and short lifetime. To improve these aspects, the current research activity focuses on the development of robust and generalizable diagnostic techniques, aimed at detecting and isolating faults within the entire system (i.e. SOFC stack and balance of plant). Coupled with appropriate recovery strategies, diagnosis can prevent undesired system shutdowns during faulty conditions, with consequent lifetime increase and maintenance costs reduction. This paper deals with the on-line experimental validation of a model-based diagnostic algorithm applied to a pre-commercial SOFC system. The proposed algorithm exploits a Fault Signature Matrix based on a Fault Tree Analysis and improved through fault simulations. The algorithm is characterized on the considered system and it is validated by means of experimental induction of faulty states in controlled conditions.

Structures vibration control via Tuned Mass Dampers using a co-evolution Coral Reefs Optimization algorithm

NASA Astrophysics Data System (ADS)

Salcedo-Sanz, S.; Camacho-Gómez, C.; Magdaleno, A.; Pereira, E.; Lorenzana, A.

2017-04-01

In this paper we tackle a problem of optimal design and location of Tuned Mass Dampers (TMDs) for structures subjected to earthquake ground motions, using a novel meta-heuristic algorithm. Specifically, the Coral Reefs Optimization (CRO) with Substrate Layer (CRO-SL) is proposed as a competitive co-evolution algorithm with different exploration procedures within a single population of solutions. The proposed approach is able to solve the TMD design and location problem, by exploiting the combination of different types of searching mechanisms. This promotes a powerful evolutionary-like algorithm for optimization problems, which is shown to be very effective in this particular problem of TMDs tuning. The proposed algorithm's performance has been evaluated and compared with several reference algorithms in two building models with two and four floors, respectively.

Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces

NASA Astrophysics Data System (ADS)

Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I.; Shenoy, Krishna V.; Boahen, Kwabena

2013-06-01

Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system’s robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

Design of a multi-arm randomized clinical trial with no control arm.

PubMed

Magaret, Amalia; Angus, Derek C; Adhikari, Neill K J; Banura, Patrick; Kissoon, Niranjan; Lawler, James V; Jacob, Shevin T

2016-01-01

Clinical trial designs that include multiple treatments are currently limited to those that perform pairwise comparisons of each investigational treatment to a single control. However, there are settings, such as the recent Ebola outbreak, in which no treatment has been demonstrated to be effective; and therefore, no standard of care exists which would serve as an appropriate control. For illustrative purposes, we focused on the care of patients presenting in austere settings with critically ill 'sepsis-like' syndromes. Our approach involves a novel algorithm for comparing mortality among arms without requiring a single fixed control. The algorithm allows poorly-performing arms to be dropped during interim analyses. Consequently, the study may be completed earlier than planned. We used simulation to determine operating characteristics for the trial and to estimate the required sample size. We present a potential study design targeting a minimal effect size of a 23% relative reduction in mortality between any pair of arms. Using estimated power and spurious significance rates from the simulated scenarios, we show that such a trial would require 2550 participants. Over a range of scenarios, our study has 80 to 99% power to select the optimal treatment. Using a fixed control design, if the control arm is least efficacious, 640 subjects would be enrolled into the least efficacious arm, while our algorithm would enroll between 170 and 430. This simulation method can be easily extended to other settings or other binary outcomes. Early dropping of arms is efficient and ethical when conducting clinical trials with multiple arms. Copyright © 2015 Elsevier Inc. All rights reserved.

Field-programmable analogue arrays for the sensorless control of DC motors

NASA Astrophysics Data System (ADS)

Rivera, J.; Dueñas, I.; Ortega, S.; Del Valle, J. L.

2018-02-01

This work presents the analogue implementation of a sensorless controller for direct current motors based on the super-twisting (ST) sliding mode technique, by means of field programmable analogue arrays (FPAA). The novelty of this work is twofold, first is the use of the ST algorithm in a sensorless scheme for DC motors, and the implementation method of this type of sliding mode controllers in FPAAs. The ST algorithm reduces the chattering problem produced with the deliberate use of the sign function in classical sliding mode approaches. On the other hand, the advantages of the implementation method over a digital one are that the controller is not digitally approximated, the controller gains are not fine tuned and the implementation does not require the use of analogue-to-digital and digital-to-analogue converter circuits. In addition to this, the FPAA is a reconfigurable, lower cost and power consumption technology. Simulation and experimentation results were registered, where a more accurate transient response and lower power consumption were obtained by the proposed implementation method when compared to a digital implementation. Also, a more accurate performance by the DC motor is obtained with proposed sensorless ST technique when compared with a classical sliding mode approach.

Intelligent systems approach for automated identification of individual control behavior of a human operator

NASA Astrophysics Data System (ADS)

Zaychik, Kirill B.

Acceptable results have been obtained using conventional techniques to model the generic human operator's control behavior. However, little research has been done in an attempt to identify an individual based on his/her control behavior. The main hypothesis investigated in this dissertation is that different operators exhibit different control behavior when performing a given control task. Furthermore, inter-person differences are manifested in the amplitude and frequency content of the non-linear component of the control behavior. Two enhancements to the existing models of the human operator, which allow personalization of the modeled control behavior, are presented in this dissertation. One of the proposed enhancements accounts for the "testing" control signals, which are introduced by an operator for more accurate control of the system and/or to adjust his/her control strategy. Such enhancement uses the Artificial Neural Network (ANN), which can be fine-tuned to model the "testing" control behavior of a given individual. The other model enhancement took the form of an equiripple filter (EF), which conditions the power spectrum of the control signal before it is passed through the plant dynamics block. The filter design technique uses Parks-McClellan algorithm, which allows parameterization of the desired levels of power at certain frequencies. A novel automated parameter identification technique (APID) was developed to facilitate the identification process of the parameters of the selected models of the human operator. APID utilizes a Genetic Algorithm (GA) based optimization engine called the Bit-climbing Algorithm (BCA). Proposed model enhancements were validated using the experimental data obtained at three different sources: the Manual Control Laboratory software experiments, Unmanned Aerial Vehicle simulation, and NASA Langley Research Center Visual Motion Simulator studies. Validation analysis involves comparison of the actual and simulated control activity signals. Validation criteria used in this dissertation is based on comparing Power Spectral Densities of the control signals against that of the Precision model of the human operator. This dissertation also addresses the issue of applying the proposed human operator model augmentation to evaluate the effectiveness of the motion feedback when simulating the actual pilot control behavior in a flight simulator. The proposed modeling methodology allows for quantitative assessments and prediction of the need for platform motion, while performing aircraft/pilot simulation studies.

Solar Photovoltaic (PV) Distributed Generation Systems - Control and Protection

NASA Astrophysics Data System (ADS)

Yi, Zhehan

This dissertation proposes a comprehensive control, power management, and fault detection strategy for solar photovoltaic (PV) distribution generations. Battery storages are typically employed in PV systems to mitigate the power fluctuation caused by unstable solar irradiance. With AC and DC loads, a PV-battery system can be treated as a hybrid microgrid which contains both DC and AC power resources and buses. In this thesis, a control power and management system (CAPMS) for PV-battery hybrid microgrid is proposed, which provides 1) the DC and AC bus voltage and AC frequency regulating scheme and controllers designed to track set points; 2) a power flow management strategy in the hybrid microgrid to achieve system generation and demand balance in both grid-connected and islanded modes; 3) smooth transition control during grid reconnection by frequency and phase synchronization control between the main grid and microgrid. Due to the increasing demands for PV power, scales of PV systems are getting larger and fault detection in PV arrays becomes challenging. High-impedance faults, low-mismatch faults, and faults occurred in low irradiance conditions tend to be hidden due to low fault currents, particularly, when a PV maximum power point tracking (MPPT) algorithm is in-service. If remain undetected, these faults can considerably lower the output energy of solar systems, damage the panels, and potentially cause fire hazards. In this dissertation, fault detection challenges in PV arrays are analyzed in depth, considering the crossing relations among the characteristics of PV, interactions with MPPT algorithms, and the nature of solar irradiance. Two fault detection schemes are then designed as attempts to address these technical issues, which detect faults inside PV arrays accurately even under challenging circumstances, e.g., faults in low irradiance conditions or high-impedance faults. Taking advantage of multi-resolution signal decomposition (MSD), a powerful signal processing technique based on discrete wavelet transformation (DWT), the first attempt is devised, which extracts the features of both line-to-line (L-L) and line-to-ground (L-G) faults and employs a fuzzy inference system (FIS) for the decision-making stage of fault detection. This scheme is then improved as the second attempt by further studying the system's behaviors during L-L faults, extracting more efficient fault features, and devising a more advanced decision-making stage: the two-stage support vector machine (SVM). For the first time, the two-stage SVM method is proposed in this dissertation to detect L-L faults in PV system with satisfactory accuracies. Numerous simulation and experimental case studies are carried out to verify the proposed control and protection strategies. Simulation environment is set up using the PSCAD/EMTDC and Matlab/Simulink software packages. Experimental case studies are conducted in a PV-battery hybrid microgrid using the dSPACE real-time controller to demonstrate the ease of hardware implementation and the controller performance. Another small-scale grid-connected PV system is set up to verify both fault detection algorithms which demonstrate promising performances and fault detecting accuracies.

An adaptive scale factor based MPPT algorithm for changing solar irradiation levels in outer space

NASA Astrophysics Data System (ADS)

Kwan, Trevor Hocksun; Wu, Xiaofeng

2017-03-01

Maximum power point tracking (MPPT) techniques are popularly used for maximizing the output of solar panels by continuously tracking the maximum power point (MPP) of their P-V curves, which depend both on the panel temperature and the input insolation. Various MPPT algorithms have been studied in literature, including perturb and observe (P&O), hill climbing, incremental conductance, fuzzy logic control and neural networks. This paper presents an algorithm which improves the MPP tracking performance by adaptively scaling the DC-DC converter duty cycle. The principle of the proposed algorithm is to detect the oscillation by checking the sign (ie. direction) of the duty cycle perturbation between the current and previous time steps. If there is a difference in the signs then it is clear an oscillation is present and the DC-DC converter duty cycle perturbation is subsequently scaled down by a constant factor. By repeating this process, the steady state oscillations become negligibly small which subsequently allows for a smooth steady state MPP response. To verify the proposed MPPT algorithm, a simulation involving irradiances levels that are typically encountered in outer space is conducted. Simulation and experimental results prove that the proposed algorithm is fast and stable in comparison to not only the conventional fixed step counterparts, but also to previous variable step size algorithms.

Experimental comparison of PV-smoothing controllers using distributed generators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Jay Dean; Ellis, Abraham; Denda, Atsushi

The power output variability of photovoltaic systems can affect local electrical grids in locations with high renewable energy penetrations or weak distribution or transmission systems. In those rare cases, quick controllable generators (e.g., energy storage systems) or loads can counteract the destabilizing effects by compensating for the power fluctuations. Previously, control algorithms for coordinated and uncoordinated operation of a small natural gas engine-generator (genset) and a battery for smoothing PV plant output were optimized using MATLAB/Simulink simulations. The simulations demonstrated that a traditional generation resource such as a natural gas genset in combination with a battery would smooth the photovoltaicmore » output while using a smaller battery state of charge (SOC) range and extending the life of the battery. This paper reports on the experimental implementation of the coordinated and uncoordinated controllers to verify the simulations and determine the differences in the controllers. The experiments were performed with the PNM PV and energy storage Prosperity site and a gas engine-generator located at the Aperture Center at Mesa Del Sol in Albuquerque, New Mexico. Two field demonstrations were performed to compare the different PV smoothing control algorithms: (1) implementing the coordinated and uncoordinated controls while switching off a subsection of the PV array at precise times on successive clear days, and (2) comparing the results of the battery and genset outputs for the coordinated control on a high variability day with simulations of the coordinated and uncoordinated controls. It was found that for certain PV power profiles the SOC range of the battery may be larger with the coordinated control, but the total amp-hours through the battery-which approximates battery wear-will always be smaller with the coordinated control.« less

Risk management algorithm for rear-side collision avoidance using a combined steering torque overlay and differential braking

NASA Astrophysics Data System (ADS)

Lee, Junyung; Yi, Kyongsu; Yoo, Hyunjae; Chong, Hyokjin; Ko, Bongchul

2015-06-01

This paper describes a risk management algorithm for rear-side collision avoidance. The proposed risk management algorithm consists of a supervisor and a coordinator. The supervisor is designed to monitor collision risks between the subject vehicle and approaching vehicle in the adjacent lane. An appropriate criterion of intervention, which satisfies high acceptance to drivers through the consideration of a realistic traffic, has been determined based on the analysis of the kinematics of the vehicles in longitudinal and lateral directions. In order to assist the driver actively and increase driver's safety, a coordinator is designed to combine lateral control using a steering torque overlay by motor-driven power steering and differential braking by vehicle stability control. In order to prevent the collision while limiting actuator's control inputs and vehicle dynamics to safe values for the assurance of the driver's comfort, the Lyapunov theory and linear matrix inequalities based optimisation methods have been used. The proposed risk management algorithm has been evaluated via simulation using CarSim and MATLAB/Simulink.

Optimized Controller Design for a 12-Pulse Voltage Source Converter Based HVDC System

NASA Astrophysics Data System (ADS)

Agarwal, Ruchi; Singh, Sanjeev

2017-12-01

The paper proposes an optimized controller design scheme for power quality improvement in 12-pulse voltage source converter based high voltage direct current system. The proposed scheme is hybrid combination of golden section search and successive linear search method. The paper aims at reduction of current sensor and optimization of controller. The voltage and current controller parameters are selected for optimization due to its impact on power quality. The proposed algorithm for controller optimizes the objective function which is composed of current harmonic distortion, power factor, and DC voltage ripples. The detailed designs and modeling of the complete system are discussed and its simulation is carried out in MATLAB-Simulink environment. The obtained results are presented to demonstrate the effectiveness of the proposed scheme under different transient conditions such as load perturbation, non-linear load condition, voltage sag condition, and tapped load fault under one phase open condition at both points-of-common coupling.

Comparing Different Fault Identification Algorithms in Distributed Power System

NASA Astrophysics Data System (ADS)

Alkaabi, Salim

A power system is a huge complex system that delivers the electrical power from the generation units to the consumers. As the demand for electrical power increases, distributed power generation was introduced to the power system. Faults may occur in the power system at any time in different locations. These faults cause a huge damage to the system as they might lead to full failure of the power system. Using distributed generation in the power system made it even harder to identify the location of the faults in the system. The main objective of this work is to test the different fault location identification algorithms while tested on a power system with the different amount of power injected using distributed generators. As faults may lead the system to full failure, this is an important area for research. In this thesis different fault location identification algorithms have been tested and compared while the different amount of power is injected from distributed generators. The algorithms were tested on IEEE 34 node test feeder using MATLAB and the results were compared to find when these algorithms might fail and the reliability of these methods.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lai, Jih-Sheng

This paper introduces control system design based softwares, SIMNON and MATLAB/SIMULINK, for power electronics system simulation. A complete power electronics system typically consists of a rectifier bridge along with its smoothing capacitor, an inverter, and a motor. The system components, featuring discrete or continuous, linear or nonlinear, are modeled in mathematical equations. Inverter control methods,such as pulse-width-modulation and hysteresis current control, are expressed in either computer algorithms or digital circuits. After describing component models and control methods, computer programs are then developed for complete systems simulation. Simulation results are mainly used for studying system performances, such as input and outputmore » current harmonics, torque ripples, and speed responses. Key computer programs and simulation results are demonstrated for educational purposes.« less

Disturbance observer based pitch control of wind turbines for disturbance rejection

NASA Astrophysics Data System (ADS)

Yuan, Yuan; Chen, Xu; Tang, Jiong

2016-04-01

In this research, a disturbance observer based (DOB) control scheme is illustrated to reject the unknown low frequency disturbances to wind turbines. Specifically, we aim at maintaining the constant output power but achieving better generator speed regulation when the wind turbine is operated at time-varying and turbulent wind field. The disturbance observer combined with a filter is designed to asymptotically reject the persistent unknown time-varying disturbances. The proposed algorithm is tested in both linearized and nonlinear NREL offshore 5-MW baseline wind turbine. The application of this DOB pitch controller achieves improved power and speed regulation in Region 3 compared with a baseline gain scheduling PID collective controller both in linearized and nonlinear plant.

RAVEN: a GUI and an Artificial Intelligence Engine in a Dynamic PRA Framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

C. Rabiti; D. Mandelli; A. Alfonsi

Increases in computational power and pressure for more accurate simulations and estimations of accident scenario consequences are driving the need for Dynamic Probabilistic Risk Assessment (PRA) [1] of very complex models. While more sophisticated algorithms and computational power address the back end of this challenge, the front end is still handled by engineers that need to extract meaningful information from the large amount of data and build these complex models. Compounding this problem is the difficulty in knowledge transfer and retention, and the increasing speed of software development. The above-described issues would have negatively impacted deployment of the new highmore » fidelity plant simulator RELAP-7 (Reactor Excursion and Leak Analysis Program) at Idaho National Laboratory. Therefore, RAVEN that was initially focused to be the plant controller for RELAP-7 will help mitigate future RELAP-7 software engineering risks. In order to accomplish this task, Reactor Analysis and Virtual Control Environment (RAVEN) has been designed to provide an easy to use Graphical User Interface (GUI) for building plant models and to leverage artificial intelligence algorithms in order to reduce computational time, improve results, and help the user to identify the behavioral pattern of the Nuclear Power Plants (NPPs). In this paper we will present the GUI implementation and its current capability status. We will also introduce the support vector machine algorithms and show our evaluation of their potentiality in increasing the accuracy and reducing the computational costs of PRA analysis. In this evaluation we will refer to preliminary studies performed under the Risk Informed Safety Margins Characterization (RISMC) project of the Light Water Reactors Sustainability (LWRS) campaign [3]. RISMC simulation needs and algorithm testing are currently used as a guidance to prioritize RAVEN developments relevant to PRA.« less

Adaptive control for solar energy based DC microgrid system development

NASA Astrophysics Data System (ADS)

Zhang, Qinhao

During the upgrading of current electric power grid, it is expected to develop smarter, more robust and more reliable power systems integrated with distributed generations. To realize these objectives, traditional control techniques are no longer effective in either stabilizing systems or delivering optimal and robust performances. Therefore, development of advanced control methods has received increasing attention in power engineering. This work addresses two specific problems in the control of solar panel based microgrid systems. First, a new control scheme is proposed for the microgrid systems to achieve optimal energy conversion ratio in the solar panels. The control system can optimize the efficiency of the maximum power point tracking (MPPT) algorithm by implementing two layers of adaptive control. Such a hierarchical control architecture has greatly improved the system performance, which is validated through both mathematical analysis and computer simulation. Second, in the development of the microgrid transmission system, the issues related to the tele-communication delay and constant power load (CPL)'s negative incremental impedance are investigated. A reference model based method is proposed for pole and zero placements that address the challenges of the time delay and CPL in closed-loop control. The effectiveness of the proposed modeling and control design methods are demonstrated in a simulation testbed. Practical aspects of the proposed methods for general microgrid systems are also discussed.

Improved power control using optimal adjustable coefficients for three-phase photovoltaic inverter under unbalanced grid voltage.

PubMed

Wang, Qianggang; Zhou, Niancheng; Lou, Xiaoxuan; Chen, Xu

2014-01-01

Unbalanced grid faults will lead to several drawbacks in the output power quality of photovoltaic generation (PV) converters, such as power fluctuation, current amplitude swell, and a large quantity of harmonics. The aim of this paper is to propose a flexible AC current generation method by selecting coefficients to overcome these problems in an optimal way. Three coefficients are brought in to tune the output current reference within the required limits of the power quality (the current harmonic distortion, the AC current peak, the power fluctuation, and the DC voltage fluctuation). Through the optimization algorithm, the coefficients can be determined aiming to generate the minimum integrated amplitudes of the active and reactive power references with the constraints of the inverter current and DC voltage fluctuation. Dead-beat controller is utilized to track the optimal current reference in a short period. The method has been verified in PSCAD/EMTDC software.

Improved Power Control Using Optimal Adjustable Coefficients for Three-Phase Photovoltaic Inverter under Unbalanced Grid Voltage

PubMed Central

Wang, Qianggang; Zhou, Niancheng; Lou, Xiaoxuan; Chen, Xu

2014-01-01

Unbalanced grid faults will lead to several drawbacks in the output power quality of photovoltaic generation (PV) converters, such as power fluctuation, current amplitude swell, and a large quantity of harmonics. The aim of this paper is to propose a flexible AC current generation method by selecting coefficients to overcome these problems in an optimal way. Three coefficients are brought in to tune the output current reference within the required limits of the power quality (the current harmonic distortion, the AC current peak, the power fluctuation, and the DC voltage fluctuation). Through the optimization algorithm, the coefficients can be determined aiming to generate the minimum integrated amplitudes of the active and reactive power references with the constraints of the inverter current and DC voltage fluctuation. Dead-beat controller is utilized to track the optimal current reference in a short period. The method has been verified in PSCAD/EMTDC software. PMID:25243215

Nonlinear time-series-based adaptive control applications

NASA Technical Reports Server (NTRS)

Mohler, R. R.; Rajkumar, V.; Zakrzewski, R. R.

1991-01-01

A control design methodology based on a nonlinear time-series reference model is presented. It is indicated by highly nonlinear simulations that such designs successfully stabilize troublesome aircraft maneuvers undergoing large changes in angle of attack as well as large electric power transients due to line faults. In both applications, the nonlinear controller was significantly better than the corresponding linear adaptive controller. For the electric power network, a flexible AC transmission system with series capacitor power feedback control is studied. A bilinear autoregressive moving average reference model is identified from system data, and the feedback control is manipulated according to a desired reference state. The control is optimized according to a predictive one-step quadratic performance index. A similar algorithm is derived for control of rapid changes in aircraft angle of attack over a normally unstable flight regime. In the latter case, however, a generalization of a bilinear time-series model reference includes quadratic and cubic terms in angle of attack.

Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface

PubMed Central

Blakely, Tim M.; Miller, Kai J.; Rao, Rajesh P. N.; Ojemann, Jeffrey G.

2014-01-01

Human subjects can learn to control a one-dimensional electrocorticographic (ECoG) brain-computer interface (BCI) using modulation of primary motor (M1) high-gamma activity (signal power in the 75–200 Hz range). However, the stability and dynamics of the signals over the course of new BCI skill acquisition have not been investigated. In this study, we report 3 characteristic periods in evolution of the high-gamma control signal during BCI training: initial, low task accuracy with corresponding low power modulation in the gamma spectrum, followed by a second period of improved task accuracy with increasing average power separation between activity and rest, and a final period of high task accuracy with stable (or decreasing) power separation and decreasing trial-to-trial variance. These findings may have implications in the design and implementation of BCI control algorithms. PMID:25599079

Emergency strategy optimization for the environmental control system in manned spacecraft

NASA Astrophysics Data System (ADS)

Li, Guoxiang; Pang, Liping; Liu, Meng; Fang, Yufeng; Zhang, Helin

2018-02-01

It is very important for a manned environmental control system (ECS) to be able to reconfigure its operation strategy in emergency conditions. In this article, a multi-objective optimization is established to design the optimal emergency strategy for an ECS in an insufficient power supply condition. The maximum ECS lifetime and the minimum power consumption are chosen as the optimization objectives. Some adjustable key variables are chosen as the optimization variables, which finally represent the reconfigured emergency strategy. The non-dominated sorting genetic algorithm-II is adopted to solve this multi-objective optimization problem. Optimization processes are conducted at four different carbon dioxide partial pressure control levels. The study results show that the Pareto-optimal frontiers obtained from this multi-objective optimization can represent the relationship between the lifetime and the power consumption of the ECS. Hence, the preferred emergency operation strategy can be recommended for situations when there is suddenly insufficient power.

Efficient Strategies for Predictive Cell-Level Control of Lithium-Ion Batteries

NASA Astrophysics Data System (ADS)

Xavier, Marcelo A.

This dissertation introduces a set of state-space based model predictive control (MPC) algorithms tailored to a non-zero feedthrough term to account for the ohmic resistance that is inherent to the battery dynamics. MPC is herein applied to the problem of regulating cell-level measures of performance for lithium-ion batteries; the control methodologies are used first to compute a fast charging profile that respects input, output, and state constraints, i.e., input current, terminal voltage, and state of charge for an equivalent circuit model of the battery cell, and extended later to a linearized physics-based reduced-order model. The novelty of this work can summarized as follows: (1) the MPC variants are employed to a physics based reduce-order model in order to make use of the available set of internal electrochemical variables and mitigate internal mechanisms of cell degradation. (e.g., lithium plating); (2) we developed a dual-mode MPC closed-loop paradigm that suits the battery control problem with the objective of reducing computational effort by solving simpler optimization routines and guaranteeing stability; and finally (3) we developed a completely new approach of the use of a predictive control strategy where MPC is employed as a "smart sensor" for power estimation. Results are presented that show the comparative performance of the MPC algorithms for both EMC and PBROM These results highlight that dual-mode MPC can deliver optimal input current profiles by using a shorter horizon while still guaranteeing stability. Additionally, rigorous mathematical developments are presented for the development of the MPC algorithms. The use of MPC as a "smart sensor" presents it self as an appealing method for power estimation, since MPC permits a fully dynamic input profile that is able to achieve performance right at the proper constraint boundaries. Therefore, MPC is expected to produce accurate power limits for each computed sample time when compared to the Bisection method [1] which assumes constant input values over the prediction interval.

Description of the SSF PMAD DC testbed control system data acquisition function

NASA Technical Reports Server (NTRS)

Baez, Anastacio N.; Mackin, Michael; Wright, Theodore

1992-01-01

The NASA LeRC in Cleveland, Ohio has completed the development and integration of a Power Management and Distribution (PMAD) DC Testbed. This testbed is a reduced scale representation of the end to end, sources to loads, Space Station Freedom Electrical Power System (SSF EPS). This unique facility is being used to demonstrate DC power generation and distribution, power management and control, and system operation techniques considered to be prime candidates for the Space Station Freedom. A key capability of the testbed is its ability to be configured to address system level issues in support of critical SSF program design milestones. Electrical power system control and operation issues like source control, source regulation, system fault protection, end-to-end system stability, health monitoring, resource allocation, and resource management are being evaluated in the testbed. The SSF EPS control functional allocation between on-board computers and ground based systems is evolving. Initially, ground based systems will perform the bulk of power system control and operation. The EPS control system is required to continuously monitor and determine the current state of the power system. The DC Testbed Control System consists of standard controllers arranged in a hierarchical and distributed architecture. These controllers provide all the monitoring and control functions for the DC Testbed Electrical Power System. Higher level controllers include the Power Management Controller, Load Management Controller, Operator Interface System, and a network of computer systems that perform some of the SSF Ground based Control Center Operation. The lower level controllers include Main Bus Switch Controllers and Photovoltaic Controllers. Power system status information is periodically provided to the higher level controllers to perform system control and operation. The data acquisition function of the control system is distributed among the various levels of the hierarchy. Data requirements are dictated by the control system algorithms being implemented at each level. A functional description of the various levels of the testbed control system architecture, the data acquisition function, and the status of its implementationis presented.

Motion Cueing Algorithm Development: Piloted Performance Testing of the Cueing Algorithms

NASA Technical Reports Server (NTRS)

Houck, Jacob A. (Technical Monitor); Telban, Robert J.; Cardullo, Frank M.; Kelly, Lon C.

2005-01-01

The relative effectiveness in simulating aircraft maneuvers with both current and newly developed motion cueing algorithms was assessed with an eleven-subject piloted performance evaluation conducted on the NASA Langley Visual Motion Simulator (VMS). In addition to the current NASA adaptive algorithm, two new cueing algorithms were evaluated: the optimal algorithm and the nonlinear algorithm. The test maneuvers included a straight-in approach with a rotating wind vector, an offset approach with severe turbulence and an on/off lateral gust that occurs as the aircraft approaches the runway threshold, and a takeoff both with and without engine failure after liftoff. The maneuvers were executed with each cueing algorithm with added visual display delay conditions ranging from zero to 200 msec. Two methods, the quasi-objective NASA Task Load Index (TLX), and power spectral density analysis of pilot control, were used to assess pilot workload. Piloted performance parameters for the approach maneuvers, the vertical velocity upon touchdown and the runway touchdown position, were also analyzed but did not show any noticeable difference among the cueing algorithms. TLX analysis reveals, in most cases, less workload and variation among pilots with the nonlinear algorithm. Control input analysis shows pilot-induced oscillations on a straight-in approach were less prevalent compared to the optimal algorithm. The augmented turbulence cues increased workload on an offset approach that the pilots deemed more realistic compared to the NASA adaptive algorithm. The takeoff with engine failure showed the least roll activity for the nonlinear algorithm, with the least rudder pedal activity for the optimal algorithm.

Modelling and Prediction of Spark-ignition Engine Power Performance Using Incremental Least Squares Support Vector Machines

NASA Astrophysics Data System (ADS)

Wong, Pak-kin; Vong, Chi-man; Wong, Hang-cheong; Li, Ke

2010-05-01

Modern automotive spark-ignition (SI) power performance usually refers to output power and torque, and they are significantly affected by the setup of control parameters in the engine management system (EMS). EMS calibration is done empirically through tests on the dynamometer (dyno) because no exact mathematical engine model is yet available. With an emerging nonlinear function estimation technique of Least squares support vector machines (LS-SVM), the approximate power performance model of a SI engine can be determined by training the sample data acquired from the dyno. A novel incremental algorithm based on typical LS-SVM is also proposed in this paper, so the power performance models built from the incremental LS-SVM can be updated whenever new training data arrives. With updating the models, the model accuracies can be continuously increased. The predicted results using the estimated models from the incremental LS-SVM are good agreement with the actual test results and with the almost same average accuracy of retraining the models from scratch, but the incremental algorithm can significantly shorten the model construction time when new training data arrives.

128×128 three-dimensional MEMS optical switch module with simultaneous optical path connection for optical cross-connect systems.

PubMed

Mizukami, Masato; Yamaguchi, Joji; Nemoto, Naru; Kawajiri, Yuko; Hirata, Hirooki; Uchiyama, Shingo; Makihara, Mitsuhiro; Sakata, Tomomi; Shimoyama, Nobuhiro; Oda, Kazuhiro

2011-07-20

A 128×128 three-dimensional MEMS optical switch module and a switching-control algorithm for high-speed connection and optical power stabilization are described. A prototype switch module enables the simultaneous switching of all optical paths. The insertion loss is less than 4.6 dB and is 2.3 dB on average. The switching time is less than 38 ms and is 8 ms on average. We confirmed that the maximum optical power can be obtained and optical power stabilization control is possible. The results confirm that the module is suitable for practical use in optical cross-connect systems. © 2011 Optical Society of America

A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

DOE PAGES

Malikopoulos, Andreas

2015-01-01

The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain and we show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion.more » Both solutions achieved the same cumulative fuel consumption demonstrating that the online Pareto control policy is an optimal control policy.« less

Current limiting remote power control module

NASA Technical Reports Server (NTRS)

Hopkins, Douglas C.

1990-01-01

The power source for the Space Station Freedom will be fully utilized nearly all of the time. As such, any loads on the system will need to operate within expected limits. Should any load draw an inordinate amount of power, the bus voltage for the system may sag and disrupt the operation of other loads. To protect the bus and loads some type of power interface between the bus and each load must be provided. This interface is most crucial when load faults occur. A possible system configuration is presented. The proposed interface is the Current Limiting Remote Power Controller (CL-RPC). Such an interface should provide the following power functions: limit overloading and resulting undervoltage; prevent catastrophic failure and still provide for redundancy management within the load; minimize cable heating; and provide accurate current measurement. A functional block diagram of the power processing stage of a CL-RPC is included. There are four functions that drive the circuit design: rate control of current; current sensing; the variable conductance switch (VCS) technology; and the algorithm used for current limiting. Each function is discussed separately.

A reliable algorithm for optimal control synthesis

NASA Technical Reports Server (NTRS)

Vansteenwyk, Brett; Ly, Uy-Loi

1992-01-01

In recent years, powerful design tools for linear time-invariant multivariable control systems have been developed based on direct parameter optimization. In this report, an algorithm for reliable optimal control synthesis using parameter optimization is presented. Specifically, a robust numerical algorithm is developed for the evaluation of the H(sup 2)-like cost functional and its gradients with respect to the controller design parameters. The method is specifically designed to handle defective degenerate systems and is based on the well-known Pade series approximation of the matrix exponential. Numerical test problems in control synthesis for simple mechanical systems and for a flexible structure with densely packed modes illustrate positively the reliability of this method when compared to a method based on diagonalization. Several types of cost functions have been considered: a cost function for robust control consisting of a linear combination of quadratic objectives for deterministic and random disturbances, and one representing an upper bound on the quadratic objective for worst case initial conditions. Finally, a framework for multivariable control synthesis has been developed combining the concept of closed-loop transfer recovery with numerical parameter optimization. The procedure enables designers to synthesize not only observer-based controllers but also controllers of arbitrary order and structure. Numerical design solutions rely heavily on the robust algorithm due to the high order of the synthesis model and the presence of near-overlapping modes. The design approach is successfully applied to the design of a high-bandwidth control system for a rotorcraft.

Optimal dynamic control of resources in a distributed system

NASA Technical Reports Server (NTRS)

Shin, Kang G.; Krishna, C. M.; Lee, Yann-Hang

1989-01-01

The authors quantitatively formulate the problem of controlling resources in a distributed system so as to optimize a reward function and derive optimal control strategies using Markov decision theory. The control variables treated are quite general; they could be control decisions related to system configuration, repair, diagnostics, files, or data. Two algorithms for resource control in distributed systems are derived for time-invariant and periodic environments, respectively. A detailed example to demonstrate the power and usefulness of the approach is provided.

Control of an innovative super-capacitor-powered shape-memory-alloy actuated accumulator for blowout preventer

NASA Astrophysics Data System (ADS)

Chen, Jian; Li, Peng; Song, Gangbing; Ren, Zhang

2017-01-01

The design of a super-capacitor-powered shape-memory-alloy (SMA) actuated accumulator for blowout preventer (BOP) presented in this paper featured several advantages over conventional hydraulic accumulators including instant large current drive, quick system response and elimination of need for the pressure conduits. However, the mechanical design introduced two challenges, the nonlinear nature of SMA actuators and the varying voltage provided by a super capacitor, for control system design. A cerebellar model articulation controller (CMAC) feedforward plus PID controller was developed with the aim of compensation for these adverse effects. Experiments were conducted on a scaled down model and experimental results show that precision control can be achieved with the proposed configurations and algorithms.

Parameter selection in limited data cone-beam CT reconstruction using edge-preserving total variation algorithms

NASA Astrophysics Data System (ADS)

Lohvithee, Manasavee; Biguri, Ander; Soleimani, Manuchehr

2017-12-01

There are a number of powerful total variation (TV) regularization methods that have great promise in limited data cone-beam CT reconstruction with an enhancement of image quality. These promising TV methods require careful selection of the image reconstruction parameters, for which there are no well-established criteria. This paper presents a comprehensive evaluation of parameter selection in a number of major TV-based reconstruction algorithms. An appropriate way of selecting the values for each individual parameter has been suggested. Finally, a new adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm is presented, which implements the edge-preserving function for CBCT reconstruction with limited data. The proposed algorithm shows significant robustness compared to three other existing algorithms: ASD-POCS, AwASD-POCS and PCSD. The proposed AwPCSD algorithm is able to preserve the edges of the reconstructed images better with fewer sensitive parameters to tune.

A novel topology control approach to maintain the node degree in dynamic wireless sensor networks.

PubMed

Huang, Yuanjiang; Martínez, José-Fernán; Díaz, Vicente Hernández; Sendra, Juana

2014-03-07

Topology control is an important technique to improve the connectivity and the reliability of Wireless Sensor Networks (WSNs) by means of adjusting the communication range of wireless sensor nodes. In this paper, a novel Fuzzy-logic Topology Control (FTC) is proposed to achieve any desired average node degree by adaptively changing communication range, thus improving the network connectivity, which is the main target of FTC. FTC is a fully localized control algorithm, and does not rely on location information of neighbors. Instead of designing membership functions and if-then rules for fuzzy-logic controller, FTC is constructed from the training data set to facilitate the design process. FTC is proved to be accurate, stable and has short settling time. In order to compare it with other representative localized algorithms (NONE, FLSS, k-Neighbor and LTRT), FTC is evaluated through extensive simulations. The simulation results show that: firstly, similar to k-Neighbor algorithm, FTC is the best to achieve the desired average node degree as node density varies; secondly, FTC is comparable to FLSS and k-Neighbor in terms of energy-efficiency, but is better than LTRT and NONE; thirdly, FTC has the lowest average maximum communication range than other algorithms, which indicates that the most energy-consuming node in the network consumes the lowest power.

Artificial neural networks for control of a grid-connected rectifier/inverter under disturbance, dynamic and power converter switching conditions.

PubMed

Li, Shuhui; Fairbank, Michael; Johnson, Cameron; Wunsch, Donald C; Alonso, Eduardo; Proaño, Julio L

2014-04-01

Three-phase grid-connected converters are widely used in renewable and electric power system applications. Traditionally, grid-connected converters are controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show limitations in their applicability to dynamic systems. This paper investigates how to mitigate such restrictions using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming algorithm and is trained by using back-propagation through time. To enhance performance and stability under disturbance, additional strategies are adopted, including the use of integrals of error signals to the network inputs and the introduction of grid disturbance voltage to the outputs of a well-trained network. The performance of the neural-network controller is studied under typical vector control conditions and compared against conventional vector control methods, which demonstrates that the neural vector control strategy proposed in this paper is effective. Even in dynamic and power converter switching environments, the neural vector controller shows strong ability to trace rapidly changing reference commands, tolerate system disturbances, and satisfy control requirements for a faulted power system.

Development of Automatic Controller of Brain Temperature Based on the Conditions of Clinical Use

NASA Astrophysics Data System (ADS)

Utsuki, Tomohiko; Wakamatsu, Hidetoshi

A new automatic controller of brain temperature was developed based on the inevitable conditions of its clinical use from the viewpoint of various kinds of feasibility, in particular, electric power consumption of less than 1,500W in ICU. The adaptive algorithm was employed to cope with individual time-varying characteristic change of patients. The controller under water-surface cooling hypothermia requires much power for the frequent regulation of the water temperature of cooling blankets. Thus, in this study, the power consumption of the controller was checked by several kinds of examinations involving the control simulation of brain temperature using a mannequin with thermal characteristics similar to that of adult patients. The required accuracy of therapeutic brain hypothermia, i.e. control deviation within ±0.1C was experimentally confirmed using “root mean square of the control error”, despite the present controller consumes less energy comparing with the one in the case of our conventional controller, where it can still keeps remaining power margin more than 300W even in the full operation. Thereby, the clinically required water temperature was also confirmed within the limit of power supply, thus its practical application is highly expected with less physical burden of medical staff inclusive of more usability and more medical cost performance.

Design, Control, and Modeling of a New Voltage Source Converter for HVDC System

NASA Astrophysics Data System (ADS)

Mohan, Madhan; Singh, Bhim; Ketan Panigrahi, Bijaya

2013-05-01

Abstract: A New Voltage Source Converter (VSC) based on neutral clamped three-level circuit is proposed for High Voltage DC (HVDC) system. The proposed VSC is designed in a multipulse configuration. The converter is operated by Fundamental Frequency Switching (FFS). A new control method is developed for achieving all the necessary control aspects of HVDC system such as independent real and reactive power control, bidirectional real and reactive power control. The basic of the control method is varying the pulse width and by keeping the dc link voltage constant. The steady state and dynamic performances of HVDC system interconnecting two different frequencies network are demonstrated for active and reactive power control. Total number of transformers used in this system are reduced to half in comparison with the two-level VSCs for both active and reactive power control. The performance of the HVDC system is improved in terms of reduced harmonics level even at fundamental frequency switching. The harmonic performance of the designed converter is also studied for different value of the dead angle (β), and the optimized range of the dead angle is achieved for varying reactive power requirement. Simulation results are presented for the designed three level multipulse voltage source converters with the proposed control algorithm.

Feedback Power Control Strategies in Wireless Sensor Networks with Joint Channel Decoding

PubMed Central

Abrardo, Andrea; Ferrari, Gianluigi; Martalò, Marco; Perna, Fabio

2009-01-01

In this paper, we derive feedback power control strategies for block-faded multiple access schemes with correlated sources and joint channel decoding (JCD). In particular, upon the derivation of the feasible signal-to-noise ratio (SNR) region for the considered multiple access schemes, i.e., the multidimensional SNR region where error-free communications are, in principle, possible, two feedback power control strategies are proposed: (i) a classical feedback power control strategy, which aims at equalizing all link SNRs at the access point (AP), and (ii) an innovative optimized feedback power control strategy, which tries to make the network operational point fall in the feasible SNR region at the lowest overall transmit energy consumption. These strategies will be referred to as “balanced SNR” and “unbalanced SNR,” respectively. While they require, in principle, an unlimited power control range at the sources, we also propose practical versions with a limited power control range. We preliminary consider a scenario with orthogonal links and ideal feedback. Then, we analyze the robustness of the proposed power control strategies to possible non-idealities, in terms of residual multiple access interference and noisy feedback channels. Finally, we successfully apply the proposed feedback power control strategies to a limiting case of the class of considered multiple access schemes, namely a central estimating officer (CEO) scenario, where the sensors observe noisy versions of a common binary information sequence and the AP's goal is to estimate this sequence by properly fusing the soft-output information output by the JCD algorithm. PMID:22291536

Variable Cadence Walking and Ground Adaptive Standing with a Powered Ankle Prosthesis

PubMed Central

Shultz, Amanda H.; Lawson, Brian E.; Goldfarb, Michael

2015-01-01

Abstract This paper describes a control approach that provides walking and standing functionality for a powered ankle prosthesis, and demonstrates the efficacy of the approach in experiments in which a unilateral transtibial amputee subject walks with the prosthesis at variable cadences, and stands on various slopes. Both controllers incorporate a finite-state structure that emulates healthy ankle joint behavior via a series of piecewise passive impedance functions. The walking controller incorporates an algorithm to modify impedance parameters based on estimated cadence, while the standing controller incorporates an algorithm to modulate the ankle equilibrium angle in order to adapt to the ground slope and user posture, and the supervisory controller selects between the walking and standing controllers. The system is shown to reproduce several essential biomechanical features of the healthy joint during walking, particularly relative to a passive prosthesis, and is shown to adapt to variable cadences. The system is also shown to adapt to slopes over a range of ± 15 deg and to provide support to the user in a manner that is biomimetic, as validated by quasi-static stiffness measurements recorded by the prosthesis. Data from standing trials indicate that the user places more weight on the powered prosthesis than on his passive prosthesis when standing on sloped surfaces, particularly at angles of 10 deg or greater. The authors also demonstrated that the prosthesis typically began providing support within 1 s of initial contact with the ground. Further, the supervisory controller was shown to be effective in switching between walking and standing, as well as in determining ground slope just prior to the transition from the standing controller to the walking controller, where the estimated ground slope was within 1.25 deg of the actual ground slope for all trials. PMID:25955789

Autonomous Control of Nuclear Power Plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Basher, H.

2003-10-20

A nuclear reactor is a complex system that requires highly sophisticated controllers to ensure that desired performance and safety can be achieved and maintained during its operations. Higher-demanding operational requirements such as reliability, lower environmental impacts, and improved performance under adverse conditions in nuclear power plants, coupled with the complexity and uncertainty of the models, necessitate the use of an increased level of autonomy in the control methods. In the opinion of many researchers, the tasks involved during nuclear reactor design and operation (e.g., design optimization, transient diagnosis, and core reload optimization) involve important human cognition and decisions that maymore » be more easily achieved with intelligent methods such as expert systems, fuzzy logic, neural networks, and genetic algorithms. Many experts in the field of control systems share the idea that a higher degree of autonomy in control of complex systems such as nuclear plants is more easily achievable through the integration of conventional control systems and the intelligent components. Researchers have investigated the feasibility of the integration of fuzzy logic, neural networks, genetic algorithms, and expert systems with the conventional control methods to achieve higher degrees of autonomy in different aspects of reactor operations such as reactor startup, shutdown in emergency situations, fault detection and diagnosis, nuclear reactor alarm processing and diagnosis, and reactor load-following operations, to name a few. With the advancement of new technologies and computing power, it is feasible to automate most of the nuclear reactor control and operation, which will result in increased safety and economical benefits. This study surveys current status, practices, and recent advances made towards developing autonomous control systems for nuclear reactors.« less

Optimal Regulation of Virtual Power Plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall Anese, Emiliano; Guggilam, Swaroop S.; Simonetto, Andrea

This paper develops a real-time algorithmic framework for aggregations of distributed energy resources (DERs) in distribution networks to provide regulation services in response to transmission-level requests. Leveraging online primal-dual-type methods for time-varying optimization problems and suitable linearizations of the nonlinear AC power-flow equations, we believe this work establishes the system-theoretic foundation to realize the vision of distribution-level virtual power plants. The optimization framework controls the output powers of dispatchable DERs such that, in aggregate, they respond to automatic-generation-control and/or regulation-services commands. This is achieved while concurrently regulating voltages within the feeder and maximizing customers' and utility's performance objectives. Convergence andmore » tracking capabilities are analytically established under suitable modeling assumptions. Simulations are provided to validate the proposed approach.« less

A new model predictive control algorithm by reducing the computing time of cost function minimization for NPC inverter in three-phase power grids.

PubMed

Taheri, Asghar; Zhalebaghi, Mohammad Hadi

2017-11-01

This paper presents a new control strategy based on finite-control-set model-predictive control (FCS-MPC) for Neutral-point-clamped (NPC) three-level converters. Containing some advantages like fast dynamic response, easy inclusion of constraints and simple control loop, makes the FCS-MPC method attractive to use as a switching strategy for converters. However, the large amount of required calculations is a problem in the widespread of this method. In this way, to resolve this problem this paper presents a modified method that effectively reduces the computation load compare with conventional FCS-MPC method and at the same time does not affect on control performance. The proposed method can be used for exchanging power between electrical grid and DC resources by providing active and reactive power compensations. Experiments on three-level converter for three Power Factor Correction (PFC), inductive and capacitive compensation modes verify the good and comparable performance. The results have been simulated using MATLAB/SIMULINK software. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal clustering of MGs based on droop controller for improving reliability using a hybrid of harmony search and genetic algorithms.

PubMed

Abedini, Mohammad; Moradi, Mohammad H; Hosseinian, S M

2016-03-01

This paper proposes a novel method to address reliability and technical problems of microgrids (MGs) based on designing a number of self-adequate autonomous sub-MGs via adopting MGs clustering thinking. In doing so, a multi-objective optimization problem is developed where power losses reduction, voltage profile improvement and reliability enhancement are considered as the objective functions. To solve the optimization problem a hybrid algorithm, named HS-GA, is provided, based on genetic and harmony search algorithms, and a load flow method is given to model different types of DGs as droop controller. The performance of the proposed method is evaluated in two case studies. The results provide support for the performance of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Coordinated control system modelling of ultra-supercritical unit based on a new T-S fuzzy structure.

PubMed

Hou, Guolian; Du, Huan; Yang, Yu; Huang, Congzhi; Zhang, Jianhua

2018-03-01

The thermal power plant, especially the ultra-supercritical unit is featured with severe nonlinearity, strong multivariable coupling. In order to deal with these difficulties, it is of great importance to build an accurate and simple model of the coordinated control system (CCS) in the ultra-supercritical unit. In this paper, an improved T-S fuzzy model identification approach is proposed. First of all, the k-means++ algorithm is employed to identify the premise parameters so as to guarantee the number of fuzzy rules. Then, the local linearized models are determined by using the incremental historical data around the cluster centers, which are obtained via the stochastic gradient descent algorithm with momentum and variable learning rate. Finally, with the proposed method, the CCS model of a 1000 MW USC unit in Tai Zhou power plant is developed. The effectiveness of the proposed approach is validated by the given extensive simulation results, and it can be further employed to design the overall advanced controllers for the CCS in an USC unit. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Simplified and advanced modelling of traction control systems of heavy-haul locomotives

NASA Astrophysics Data System (ADS)

Spiryagin, Maksym; Wolfs, Peter; Szanto, Frank; Cole, Colin

2015-05-01

Improving tractive effort is a very complex task in locomotive design. It requires the development of not only mechanical systems but also power systems, traction machines and traction algorithms. At the initial design stage, traction algorithms can be verified by means of a simulation approach. A simple single wheelset simulation approach is not sufficient because all locomotive dynamics are not fully taken into consideration. Given that many traction control strategies exist, the best solution is to use more advanced approaches for such studies. This paper describes the modelling of a locomotive with a bogie traction control strategy based on a co-simulation approach in order to deliver more accurate results. The simplified and advanced modelling approaches of a locomotive electric power system are compared in this paper in order to answer a fundamental question. What level of modelling complexity is necessary for the investigation of the dynamic behaviours of a heavy-haul locomotive running under traction? The simulation results obtained provide some recommendations on simulation processes and the further implementation of advanced and simplified modelling approaches.

Method for controlling a motor vehicle powertrain

DOEpatents

Burba, Joseph C.; Landman, Ronald G.; Patil, Prabhakar B.; Reitz, Graydon A.

1990-01-01

A multiple forward speed automatic transmission produces its lowest forward speed ratio when a hydraulic clutch and hydraulic brake are disengaged and a one-way clutch connects a ring gear to the transmission casing. Second forward speed ratio results when the hydraulic clutch is engaged to connect the ring gear to the planetary carrier of a second gear set. Reverse drive and regenerative operation result when an hydraulic brake fixes the planetary and the direction of power flow is reversed. Various sensors produce signals representing the position of the gear selector lever operated manually by the vehicle operator, the speed of the power source, the state of the ignition key, and the rate of release of an accelerator pedal. A control algorithm produces input data representing a commanded upshift, a commanded downshift and a torque command and various constant torque signals. A microprocessor processes the input and produces a response to them in accordance with the execution of a control algorithm. Output or response signals cause selective engagement and disengagement of the clutch and brake to produce the forward drive, reverse and regenerative operation of the transmission.

Method for controlling a motor vehicle powertrain

DOEpatents

Burba, J.C.; Landman, R.G.; Patil, P.B.; Reitz, G.A.

1990-05-22

A multiple forward speed automatic transmission produces its lowest forward speed ratio when a hydraulic clutch and hydraulic brake are disengaged and a one-way clutch connects a ring gear to the transmission casing. Second forward speed ratio results when the hydraulic clutch is engaged to connect the ring gear to the planetary carrier of a second gear set. Reverse drive and regenerative operation result when an hydraulic brake fixes the planetary and the direction of power flow is reversed. Various sensors produce signals representing the position of the gear selector lever operated manually by the vehicle operator, the speed of the power source, the state of the ignition key, and the rate of release of an accelerator pedal. A control algorithm produces input data representing a commanded upshift, a commanded downshift and a torque command and various constant torque signals. A microprocessor processes the input and produces a response to them in accordance with the execution of a control algorithm. Output or response signals cause selective engagement and disengagement of the clutch and brake to produce the forward drive, reverse and regenerative operation of the transmission. 7 figs.

Rolling scheduling of electric power system with wind power based on improved NNIA algorithm

NASA Astrophysics Data System (ADS)

Xu, Q. S.; Luo, C. J.; Yang, D. J.; Fan, Y. H.; Sang, Z. X.; Lei, H.

2017-11-01

This paper puts forth a rolling modification strategy for day-ahead scheduling of electric power system with wind power, which takes the operation cost increment of unit and curtailed wind power of power grid as double modification functions. Additionally, an improved Nondominated Neighbor Immune Algorithm (NNIA) is proposed for solution. The proposed rolling scheduling model has further improved the operation cost of system in the intra-day generation process, enhanced the system’s accommodation capacity of wind power, and modified the key transmission section power flow in a rolling manner to satisfy the security constraint of power grid. The improved NNIA algorithm has defined an antibody preference relation model based on equal incremental rate, regulation deviation constraints and maximum & minimum technical outputs of units. The model can noticeably guide the direction of antibody evolution, and significantly speed up the process of algorithm convergence to final solution, and enhance the local search capability.

Multiphase power supply when inverting currents for group of Peltier elements

NASA Astrophysics Data System (ADS)

Osintsev, A. V.; Sobko, A. A.; Komnatnov, M. E.

2018-05-01

The use of multiphase power supply of a group of Peltier elements (PE) is considered to reduce the load on the power source. Schemes and a control layout with the use of the H-bridge, allowing the invert of the current through the PE, are given. The analysis of the operation of the used H-bridges and PE in the frequency range of the control PWM signal from 30 Hz to 32 kHz is performed. The algorithm for monitoring the current sensors is presented and the time diagrams of the currents are represented through the PE and H-bridges using a two-phase and four-phase control PWM signal for one, two and four phases of the supply. The results showed stable heating and cooling of the PE at frequencies from 30 Hz to 1 kHz. The use of multiphase power supply of PE made it possible to significantly reduce the load on the power source.

Adaptive Harmonic Detection Control of Grid Interfaced Solar Photovoltaic Energy System with Power Quality Improvement

NASA Astrophysics Data System (ADS)

Singh, B.; Goel, S.

2015-03-01

This paper presents a grid interfaced solar photovoltaic (SPV) energy system with a novel adaptive harmonic detection control for power quality improvement at ac mains under balanced as well as unbalanced and distorted supply conditions. The SPV energy system is capable of compensation of linear and nonlinear loads with the objectives of load balancing, harmonics elimination, power factor correction and terminal voltage regulation. The proposed control increases the utilization of PV infrastructure and brings down its effective cost due to its other benefits. The adaptive harmonic detection control algorithm is used to detect the fundamental active power component of load currents which are subsequently used for reference source currents estimation. An instantaneous symmetrical component theory is used to obtain instantaneous positive sequence point of common coupling (PCC) voltages which are used to derive inphase and quadrature phase voltage templates. The proposed grid interfaced PV energy system is modelled and simulated in MATLAB Simulink and its performance is verified under various operating conditions.

Single-stage three-phase boost power factor correction circuit for AC-DC converter

NASA Astrophysics Data System (ADS)

Azazi, Haitham Z.; Ahmed, Sayed M.; Lashine, Azza E.

2018-01-01

This article presents a single-stage three-phase power factor correction (PFC) circuit for AC-to-DC converter using a single-switch boost regulator, leading to improve the input power factor (PF), reducing the input current harmonics and decreasing the number of required active switches. A novel PFC control strategy which is characterised as a simple and low-cost control circuit was adopted, for achieving a good dynamic performance, unity input PF, and minimising the harmonic contents of the input current, at which it can be applied to low/medium power converters. A detailed analytical, simulation and experimental studies were therefore conducted. The effectiveness of the proposed controller algorithm is validated by the simulation results, which were carried out using MATLAB/SIMULINK environment. The proposed system is built and tested in the laboratory using DSP-DS1104 digital control board for an inductive load. The results revealed that the total harmonic distortion in the supply current was very low. Finally, a good agreement between simulation and experimental results was achieved.

Technology achievements and projections for communication satellites of the future

NASA Technical Reports Server (NTRS)

Bagwell, J. W.

1986-01-01

Multibeam systems of the future using monolithic microwave integrated circuits to provide phase control and power gain are contrasted with discrete microwave power amplifiers from 10 to 75 W and their associated waveguide feeds, phase shifters and power splitters. Challenging new enabling technology areas include advanced electrooptical control and signal feeds. Large scale MMIC's will be used incorporating on chip control interfaces, latching, and phase and amplitude control with power levels of a few watts each. Beam forming algorithms for 80 to 90 deg. wide angle scanning and precise beam forming under wide ranging environments will be required. Satelllite systems using these dynamically reconfigured multibeam antenna systems will demand greater degrees of beam interconnectivity. Multiband and multiservice users will be interconnected through the same space platform. Monolithic switching arrays operating over a wide range of RF and IF frequencies are contrasted with current IF switch technology implemented discretely. Size, weight, and performance improvements by an order of magnitude are projected.

High-authority smart material integrated electric actuator

NASA Astrophysics Data System (ADS)

Weisensel, G. N.; Pierce, Thomas D.; Zunkel, Gary

1997-05-01

For many current applications, hydraulic power is still the preferred method of gaining mechanical advantage. However, in many of these applications, this power comes with the penalties of high weight, size, cost, and maintenance due to the system's distributed nature and redundancy requirements. A high authority smart material Integrated Electric Actuator (IEA) is a modular, self-contained linear motion device that is capable of producing dynamic output strokes similar to those of hydraulic actuators yet at significantly reduced weight and volume. It provides system simplification and miniaturization. This actuator concept has many innovative features, including a TERFENOL-D-based pump, TERFENOL-D- based active valves, control algorithms, a displacement amplification unit and integrated, unitized packaging. The IEA needs only electrical power and a control command signal as inputs to provide high authority, high response rate actuation. This approach is directly compatible with distributed control strategies. Aircraft control, automotive brakes and fuel injection, and fluid power delivery are just some examples of the IEA's pervasive applications in aerospace, defense and commercial systems.

Anticipatory control: A software retrofit for current plant controllers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parthasarathy, S.; Parlos, A.G.; Atiya, A.F.

1993-01-01

The design and simulated testing of an artificial neural network (ANN)-based self-adapting controller for complex process systems are presented in this paper. The proposed controller employs concepts based on anticipatory systems, which have been widely used in the petroleum and chemical industries, and they are slowly finding their way into the power industry. In particular, model predictive control (MPC) is used for the systematic adaptation of the controller parameters to achieve desirable plant performance over the entire operating envelope. The versatile anticipatory control algorithm developed in this study is projected to enhance plant performance and lend robustness to drifts inmore » plant parameters and to modeling uncertainties. This novel technique of integrating recurrent ANNs with a conventional controller structure appears capable of controlling complex, nonlinear, and nonminimum phase process systems. The direct, on-line adaptive control algorithm presented in this paper considers the plant response over a finite time horizon, diminishing the need for manual control or process interruption for controller gain tuning.« less

Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paul, Prokash; Bhattacharyya, Debangsu; Turton, Richard

Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimator-based control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The large-scale combinatorial optimizationmore » problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO 2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.« less

Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

DOE PAGES

Paul, Prokash; Bhattacharyya, Debangsu; Turton, Richard; ...

2017-06-06

Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimator-based control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The large-scale combinatorial optimizationmore » problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO 2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.« less

Combined magnetic and kinetic control of advanced tokamak steady state scenarios based on semi-empirical modelling

DOE PAGES

Moreau, Didier; Artaud, J. F.; Ferron, John R.; ...

2015-05-01

This paper shows that semi-empirical data-driven models based on a twotime- scale approximation for the magnetic and kinetic control of advanced tokamak (AT) scenarios can be advantageously identified from simulated rather than real data, and used for control design. The method is applied to the combined control of the safety factor profile, q(x), and normalized pressure parameter, β N, using DIII-D parameters and actuators (on-axis co-current neutral beam injection (NBI) power, off axis co-current NBI power, electron cyclotron current drive power, and ohmic coil). The approximate plasma response model was identified from simulated data obtained using a rapidly converging plasmamore » transport code, METIS, which includes an MHD equilibrium and current diffusion solver, and combines plasma transport nonlinearity with 0-D scaling laws and 1.5-D ordinary differential equations. A number of open loop simulations were performed, in which the heating and current drive (H&CD) sources were randomly modulated around the typical values of a reference AT discharge on DIIID. Using these simulated data, a two-time-scale state space model was obtained for the coupled evolution of the poloidal flux profile and βN parameter, and a controller was synthesized based on the near-optimal ARTAEMIS algorithm [D. Moreau et al., Nucl. Fusion 53 (2013) 063020]. The paper discusses the results of closed-loop nonlinear simulations, using this controller for steady state AT operation. With feedforward plus feedback control, the steady state target q-profile and β N are satisfactorily tracked with a time scale of about ten seconds, despite large disturbances applied to the feedforward powers and plasma parameters. The effectiveness of the control algorithm is thus demonstrated for long pulse and steady state high-β N AT discharges. Its robustness with respect to disturbances of the H&CD actuators and of plasma parameters such as the H-factor, plasma density and effective charge, is also shown.« less

Markov chain algorithms: a template for building future robust low-power systems

PubMed Central

Deka, Biplab; Birklykke, Alex A.; Duwe, Henry; Mansinghka, Vikash K.; Kumar, Rakesh

2014-01-01

Although computational systems are looking towards post CMOS devices in the pursuit of lower power, the expected inherent unreliability of such devices makes it difficult to design robust systems without additional power overheads for guaranteeing robustness. As such, algorithmic structures with inherent ability to tolerate computational errors are of significant interest. We propose to cast applications as stochastic algorithms based on Markov chains (MCs) as such algorithms are both sufficiently general and tolerant to transition errors. We show with four example applications—Boolean satisfiability, sorting, low-density parity-check decoding and clustering—how applications can be cast as MC algorithms. Using algorithmic fault injection techniques, we demonstrate the robustness of these implementations to transition errors with high error rates. Based on these results, we make a case for using MCs as an algorithmic template for future robust low-power systems. PMID:24842030

Online Cable Tester and Rerouter

NASA Technical Reports Server (NTRS)

Lewis, Mark; Medelius, Pedro

2012-01-01

Hardware and algorithms have been developed to transfer electrical power and data connectivity safely, efficiently, and automatically from an identified damaged/defective wire in a cable to an alternate wire path. The combination of online cable testing capabilities, along with intelligent signal rerouting algorithms, allows the user to overcome the inherent difficulty of maintaining system integrity and configuration control, while autonomously rerouting signals and functions without introducing new failure modes. The incorporation of this capability will increase the reliability of systems by ensuring system availability during operations.

Circadian Phase Resetting via Single and Multiple Control Targets

PubMed Central

Bagheri, Neda; Stelling, Jörg; Doyle, Francis J.

2008-01-01

Circadian entrainment is necessary for rhythmic physiological functions to be appropriately timed over the 24-hour day. Disruption of circadian rhythms has been associated with sleep and neuro-behavioral impairments as well as cancer. To date, light is widely accepted to be the most powerful circadian synchronizer, motivating its use as a key control input for phase resetting. Through sensitivity analysis, we identify additional control targets whose individual and simultaneous manipulation (via a model predictive control algorithm) out-perform the open-loop light-based phase recovery dynamics by nearly 3-fold. We further demonstrate the robustness of phase resetting by synchronizing short- and long-period mutant phenotypes to the 24-hour environment; the control algorithm is robust in the presence of model mismatch. These studies prove the efficacy and immediate application of model predictive control in experimental studies and medicine. In particular, maintaining proper circadian regulation may significantly decrease the chance of acquiring chronic illness. PMID:18795146

Continuous higher-order sliding mode control with time-varying gain for a class of uncertain nonlinear systems.

PubMed

Han, Yaozhen; Liu, Xiangjie

2016-05-01

This paper presents a continuous higher-order sliding mode (HOSM) control scheme with time-varying gain for a class of uncertain nonlinear systems. The proposed controller is derived from the concept of geometric homogeneity and super-twisting algorithm, and includes two parts, the first part of which achieves smooth finite time stabilization of pure integrator chains. The second part conquers the twice differentiable uncertainty and realizes system robustness by employing super-twisting algorithm. Particularly, time-varying switching control gain is constructed to reduce the switching control action magnitude to the minimum possible value while keeping the property of finite time convergence. Examples concerning the perturbed triple integrator chains and excitation control for single-machine infinite bus power system are simulated respectively to demonstrate the effectiveness and applicability of the proposed approach. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Intelligent neural network and fuzzy logic control of industrial and power systems

NASA Astrophysics Data System (ADS)

Kuljaca, Ognjen

The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of adaptive and neural network control systems, as well as for the analysis of the different algorithms such as elastic fuzzy systems.

EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimization algorithms.

PubMed

Ahirwal, M K; Kumar, Anil; Singh, G K

2013-01-01

This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.

Automated Adaptive Brightness in Wireless Capsule Endoscopy Using Image Segmentation and Sigmoid Function.

PubMed

Shrestha, Ravi; Mohammed, Shahed K; Hasan, Md Mehedi; Zhang, Xuechao; Wahid, Khan A

2016-08-01

Wireless capsule endoscopy (WCE) plays an important role in the diagnosis of gastrointestinal (GI) diseases by capturing images of human small intestine. Accurate diagnosis of endoscopic images depends heavily on the quality of captured images. Along with image and frame rate, brightness of the image is an important parameter that influences the image quality which leads to the design of an efficient illumination system. Such design involves the choice and placement of proper light source and its ability to illuminate GI surface with proper brightness. Light emitting diodes (LEDs) are normally used as sources where modulated pulses are used to control LED's brightness. In practice, instances like under- and over-illumination are very common in WCE, where the former provides dark images and the later provides bright images with high power consumption. In this paper, we propose a low-power and efficient illumination system that is based on an automated brightness algorithm. The scheme is adaptive in nature, i.e., the brightness level is controlled automatically in real-time while the images are being captured. The captured images are segmented into four equal regions and the brightness level of each region is calculated. Then an adaptive sigmoid function is used to find the optimized brightness level and accordingly a new value of duty cycle of the modulated pulse is generated to capture future images. The algorithm is fully implemented in a capsule prototype and tested with endoscopic images. Commercial capsules like Pillcam and Mirocam were also used in the experiment. The results show that the proposed algorithm works well in controlling the brightness level accordingly to the environmental condition, and as a result, good quality images are captured with an average of 40% brightness level that saves power consumption of the capsule.

Energy Center Structure Optimization by using Smart Technologies in Process Control System

NASA Astrophysics Data System (ADS)

Shilkina, Svetlana V.

2018-03-01

The article deals with practical application of fuzzy logic methods in process control systems. A control object - agroindustrial greenhouse complex, which includes its own energy center - is considered. The paper analyzes object power supply options taking into account connection to external power grids and/or installation of own power generating equipment with various layouts. The main problem of a greenhouse facility basic process is extremely uneven power consumption, which forces to purchase redundant generating equipment idling most of the time, which quite negatively affects project profitability. Energy center structure optimization is largely based on solving the object process control system construction issue. To cut investor’s costs it was proposed to optimize power consumption by building an energy-saving production control system based on a fuzzy logic controller. The developed algorithm of automated process control system functioning ensured more even electric and thermal energy consumption, allowed to propose construction of the object energy center with a smaller number of units due to their more even utilization. As a result, it is shown how practical use of microclimate parameters fuzzy control system during object functioning leads to optimization of agroindustrial complex energy facility structure, which contributes to a significant reduction in object construction and operation costs.

Power-balancing instantaneous optimization energy management for a novel series-parallel hybrid electric bus

NASA Astrophysics Data System (ADS)

Sun, Dongye; Lin, Xinyou; Qin, Datong; Deng, Tao

2012-11-01

Energy management(EM) is a core technique of hybrid electric bus(HEB) in order to advance fuel economy performance optimization and is unique for the corresponding configuration. There are existing algorithms of control strategy seldom take battery power management into account with international combustion engine power management. In this paper, a type of power-balancing instantaneous optimization(PBIO) energy management control strategy is proposed for a novel series-parallel hybrid electric bus. According to the characteristic of the novel series-parallel architecture, the switching boundary condition between series and parallel mode as well as the control rules of the power-balancing strategy are developed. The equivalent fuel model of battery is implemented and combined with the fuel of engine to constitute the objective function which is to minimize the fuel consumption at each sampled time and to coordinate the power distribution in real-time between the engine and battery. To validate the proposed strategy effective and reasonable, a forward model is built based on Matlab/Simulink for the simulation and the dSPACE autobox is applied to act as a controller for hardware in-the-loop integrated with bench test. Both the results of simulation and hardware-in-the-loop demonstrate that the proposed strategy not only enable to sustain the battery SOC within its operational range and keep the engine operation point locating the peak efficiency region, but also the fuel economy of series-parallel hybrid electric bus(SPHEB) dramatically advanced up to 30.73% via comparing with the prototype bus and a similar improvement for PBIO strategy relative to rule-based strategy, the reduction of fuel consumption is up to 12.38%. The proposed research ensures the algorithm of PBIO is real-time applicability, improves the efficiency of SPHEB system, as well as suite to complicated configuration perfectly.

Analyzing the Effect of Multi-fuel and Practical Constraints on Realistic Economic Load Dispatch using Novel Two-stage PSO

NASA Astrophysics Data System (ADS)

Chintalapudi, V. S.; Sirigiri, Sivanagaraju

2017-04-01

In power system restructuring, pricing the electrical power plays a vital role in cost allocation between suppliers and consumers. In optimal power dispatch problem, not only the cost of active power generation but also the costs of reactive power generated by the generators should be considered to increase the effectiveness of the problem. As the characteristics of reactive power cost curve are similar to that of active power cost curve, a nonconvex reactive power cost function is formulated. In this paper, a more realistic multi-fuel total cost objective is formulated by considering active and reactive power costs of generators. The formulated cost function is optimized by satisfying equality, in-equality and practical constraints using the proposed uniform distributed two-stage particle swarm optimization. The proposed algorithm is a combination of uniform distribution of control variables (to start the iterative process with good initial value) and two-stage initialization processes (to obtain best final value in less number of iterations) can enhance the effectiveness of convergence characteristics. Obtained results for the considered standard test functions and electrical systems indicate the effectiveness of the proposed algorithm and can obtain efficient solution when compared to existing methods. Hence, the proposed method is a promising method and can be easily applied to optimize the power system objectives.

Gait mode recognition and control for a portable-powered ankle-foot orthosis.

PubMed

David Li, Yifan; Hsiao-Wecksler, Elizabeth T

2013-06-01

Ankle foot orthoses (AFOs) are widely used as assistive/rehabilitation devices to correct the gait of people with lower leg neuromuscular dysfunction and muscle weakness. We have developed a portable powered ankle-foot orthosis (PPAFO), which uses a pneumatic bi-directional rotary actuator powered by compressed CO2 to provide untethered dorsiflexor and plantarflexor assistance at the ankle joint. Since portability is a key to the success of the PPAFO as an assist device, it is critical to recognize and control for gait modes (i.e. level walking, stair ascent/descent). While manual mode switching is implemented in most powered orthotic/prosthetic device control algorithms, we propose an automatic gait mode recognition scheme by tracking the 3D position of the PPAFO from an inertial measurement unit (IMU). The control scheme was designed to match the torque profile of physiological gait data during different gait modes. Experimental results indicate that, with an optimized threshold, the controller was able to identify the position, orientation and gait mode in real time, and properly control the actuation. It was also illustrated that during stair descent, a mode-specific actuation control scheme could better restore gait kinematic and kinetic patterns, compared to using the level ground controller.

RECOVERY ACT: DYNAMIC ENERGY CONSUMPTION MANAGEMENT OF ROUTING TELECOM AND DATA CENTERS THROUGH REAL-TIME OPTIMAL CONTROL (RTOC): Final Scientific/Technical Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ron Moon

This final scientific report documents the Industrial Technology Program (ITP) Stage 2 Concept Development effort on Data Center Energy Reduction and Management Through Real-Time Optimal Control (RTOC). Society is becoming increasingly dependent on information technology systems, driving exponential growth in demand for data center processing and an insatiable appetite for energy. David Raths noted, 'A 50,000-square-foot data center uses approximately 4 megawatts of power, or the equivalent of 57 barrels of oil a day1.' The problem has become so severe that in some cases, users are giving up raw performance for a better balance between performance and energy efficiency. Historically,more » power systems for data centers were crudely sized to meet maximum demand. Since many servers operate at 60%-90% of maximum power while only utilizing an average of 5% to 15% of their capability, there are huge inefficiencies in the consumption and delivery of power in these data centers. The goal of the 'Recovery Act: Decreasing Data Center Energy Use through Network and Infrastructure Control' is to develop a state of the art approach for autonomously and intelligently reducing and managing data center power through real-time optimal control. Advances in microelectronics and software are enabling the opportunity to realize significant data center power savings through the implementation of autonomous power management control algorithms. The first step to realizing these savings was addressed in this study through the successful creation of a flexible and scalable mathematical model (equation) for data center behavior and the formulation of an acceptable low technical risk market introduction strategy leveraging commercial hardware and software familiar to the data center market. Follow-on Stage 3 Concept Development efforts include predictive modeling and simulation of algorithm performance, prototype demonstrations with representative data center equipment to verify requisite performance and continued commercial partnering agreement formation to ensure uninterrupted development, and deployment of the real-time optimal control algorithm. As a software implementable technique for reducing power consumption, the RTOC has two very desirable traits supporting rapid prototyping and ultimately widespread dissemination. First, very little capital is required for implementation. No major infrastructure modifications are required and there is no need to purchase expensive capital equipment. Second, the RTOC can be rolled out incrementally. Therefore, the effectiveness can be proven without a large scale initial roll out. Through the use of the Impact Projections Model provided by the DOE, monetary savings in excess of $100M in 2020 and billions by 2040 are predicted. In terms of energy savings, the model predicts a primary energy displacement of 260 trillion BTUs (33 trillion kWh), or a 50% reduction in server power consumption. The model also predicts a corresponding reduction of pollutants such as SO2 and NOx in excess of 100,000 metric tonnes assuming the RTOC is fully deployed. While additional development and prototyping is required to validate these predictions, the relative low cost and ease of implementation compared to large capital projects makes it an ideal candidate for further investigation.« less

Intelligent Systems for Power Management and Distribution

NASA Technical Reports Server (NTRS)

Button, Robert M.

2002-01-01

The motivation behind an advanced technology program to develop intelligent power management and distribution (PMAD) systems is described. The program concentrates on developing digital control and distributed processing algorithms for PMAD components and systems to improve their size, weight, efficiency, and reliability. Specific areas of research in developing intelligent DC-DC converters and distributed switchgear are described. Results from recent development efforts are presented along with expected future benefits to the overall PMAD system performance.

The multi-disciplinary design study: A life cycle cost algorithm

NASA Technical Reports Server (NTRS)

Harding, R. R.; Pichi, F. J.

1988-01-01

The approach and results of a Life Cycle Cost (LCC) analysis of the Space Station Solar Dynamic Power Subsystem (SDPS) including gimbal pointing and power output performance are documented. The Multi-Discipline Design Tool (MDDT) computer program developed during the 1986 study has been modified to include the design, performance, and cost algorithms for the SDPS as described. As with the Space Station structural and control subsystems, the LCC of the SDPS can be computed within the MDDT program as a function of the engineering design variables. Two simple examples of MDDT's capability to evaluate cost sensitivity and design based on LCC are included. MDDT was designed to accept NASA's IMAT computer program data as input so that IMAT's detailed structural and controls design capability can be assessed with expected system LCC as computed by MDDT. No changes to IMAT were required. Detailed knowledge of IMAT is not required to perform the LCC analyses as the interface with IMAT is noninteractive.

ChIP-PaM: an algorithm to identify protein-DNA interaction using ChIP-Seq data.

PubMed

Wu, Song; Wang, Jianmin; Zhao, Wei; Pounds, Stanley; Cheng, Cheng

2010-06-03

ChIP-Seq is a powerful tool for identifying the interaction between genomic regulators and their bound DNAs, especially for locating transcription factor binding sites. However, high cost and high rate of false discovery of transcription factor binding sites identified from ChIP-Seq data significantly limit its application. Here we report a new algorithm, ChIP-PaM, for identifying transcription factor target regions in ChIP-Seq datasets. This algorithm makes full use of a protein-DNA binding pattern by capitalizing on three lines of evidence: 1) the tag count modelling at the peak position, 2) pattern matching of a specific tag count distribution, and 3) motif searching along the genome. A novel data-based two-step eFDR procedure is proposed to integrate the three lines of evidence to determine significantly enriched regions. Our algorithm requires no technical controls and efficiently discriminates falsely enriched regions from regions enriched by true transcription factor (TF) binding on the basis of ChIP-Seq data only. An analysis of real genomic data is presented to demonstrate our method. In a comparison with other existing methods, we found that our algorithm provides more accurate binding site discovery while maintaining comparable statistical power.

Robust design of multiple trailing edge flaps for helicopter vibration reduction: A multi-objective bat algorithm approach

NASA Astrophysics Data System (ADS)

Mallick, Rajnish; Ganguli, Ranjan; Seetharama Bhat, M.

2015-09-01

The objective of this study is to determine an optimal trailing edge flap configuration and flap location to achieve minimum hub vibration levels and flap actuation power simultaneously. An aeroelastic analysis of a soft in-plane four-bladed rotor is performed in conjunction with optimal control. A second-order polynomial response surface based on an orthogonal array (OA) with 3-level design describes both the objectives adequately. Two new orthogonal arrays called MGB2P-OA and MGB4P-OA are proposed to generate nonlinear response surfaces with all interaction terms for two and four parameters, respectively. A multi-objective bat algorithm (MOBA) approach is used to obtain the optimal design point for the mutually conflicting objectives. MOBA is a recently developed nature-inspired metaheuristic optimization algorithm that is based on the echolocation behaviour of bats. It is found that MOBA inspired Pareto optimal trailing edge flap design reduces vibration levels by 73% and flap actuation power by 27% in comparison with the baseline design.

Flexible operation strategy for environment control system in abnormal supply power condition

NASA Astrophysics Data System (ADS)

Liping, Pang; Guoxiang, Li; Hongquan, Qu; Yufeng, Fang

2017-04-01

This paper establishes an optimization method that can be applied to the flexible operation of the environment control system in an abnormal supply power condition. A proposed conception of lifespan is used to evaluate the depletion time of the non-regenerative substance. The optimization objective function is to maximize the lifespans. The optimization variables are the allocated powers of subsystems. The improved Non-dominated Sorting Genetic Algorithm is adopted to obtain the pareto optimization frontier with the constraints of the cabin environmental parameters and the adjustable operating parameters of the subsystems. Based on the same importance of objective functions, the preferred power allocation of subsystems can be optimized. Then the corresponding running parameters of subsystems can be determined to ensure the maximum lifespans. A long-duration space station with three astronauts is used to show the implementation of the proposed optimization method. Three different CO2 partial pressure levels are taken into consideration in this study. The optimization results show that the proposed optimization method can obtain the preferred power allocation for the subsystems when the supply power is at a less-than-nominal value. The method can be applied to the autonomous control for the emergency response of the environment control system.

Fuel sensor-less control of a liquid feed fuel cell system under steady load for portable applications

NASA Astrophysics Data System (ADS)

Chang, C. L.; Chen, C. Y.; Sung, C. C.; Liou, D. H.

This study presents a novel fuel sensor-less control scheme for a liquid feed fuel cell system that does not rely on a fuel concentration sensor. The proposed approach simplifies the design and reduces the cost and complexity of a liquid feed fuel cell system, and is especially suited to portable power sources, of which the volume and weight are important. During the reaction of a fuel cell, the cell's operating characteristics, such as potential, current and power are measured to control the supply of fuel and regulate its concentration to optimize performance. Experiments were conducted to verify that the fuel sensor-less control algorithm is effective in the liquid feed fuel cell system.

Adaptive control of artificial pancreas systems - a review.

PubMed

Turksoy, Kamuran; Cinar, Ali

2014-01-01

Artificial pancreas (AP) systems offer an important improvement in regulating blood glucose concentration for patients with type 1 diabetes, compared to current approaches. AP consists of sensors, control algorithms and an insulin pump. Different AP control algorithms such as proportional-integral-derivative, model-predictive control, adaptive control, and fuzzy logic control have been investigated in simulation and clinical studies in the past three decades. The variability over time and complexity of the dynamics of blood glucose concentration, unsteady disturbances such as meals, time-varying delays on measurements and insulin infusion, and noisy data from sensors create a challenging system to AP. Adaptive control is a powerful control technique that can deal with such challenges. In this paper, a review of adaptive control techniques for blood glucose regulation with an AP system is presented. The investigations and advances in technology produced impressive results, but there is still a need for a reliable AP system that is both commercially viable and appealing to patients with type 1 diabetes.

The control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle using a CMAC neural network.

PubMed

Harmon, Frederick G; Frank, Andrew A; Joshi, Sanjay S

2005-01-01

A Simulink model, a propulsion energy optimization algorithm, and a CMAC controller were developed for a small parallel hybrid-electric unmanned aerial vehicle (UAV). The hybrid-electric UAV is intended for military, homeland security, and disaster-monitoring missions involving intelligence, surveillance, and reconnaissance (ISR). The Simulink model is a forward-facing simulation program used to test different control strategies. The flexible energy optimization algorithm for the propulsion system allows relative importance to be assigned between the use of gasoline, electricity, and recharging. A cerebellar model arithmetic computer (CMAC) neural network approximates the energy optimization results and is used to control the parallel hybrid-electric propulsion system. The hybrid-electric UAV with the CMAC controller uses 67.3% less energy than a two-stroke gasoline-powered UAV during a 1-h ISR mission and 37.8% less energy during a longer 3-h ISR mission.

Transactive Control of Commercial Building HVAC Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Corbin, Charles D.; Makhmalbaf, Atefe; Huang, Sen

This document details the development and testing of market-based transactive controls for building heating, ventilating and air conditioning (HVAC) systems. These controls are intended to serve the purposes of reducing electricity use through conservation, reducing peak building electric demand, and providing demand flexibility to assist with power system operations. This report is the summary of the first year of work conducted under Phase 1 of the Clean Energy and Transactive Campus Project. The methods and techniques described here were first investigated in simulation, and then subsequently deployed to a physical testbed on the Pacific Northwest National Laboratory (PNNL) campus formore » validation. In this report, we describe the models and control algorithms we have developed, testing of the control algorithms in simulation, and deployment to a physical testbed. Results from physical experiments support previous simulation findings, and provide insights for further improvement.« less

Stability of power systems coupled with market dynamics

NASA Astrophysics Data System (ADS)

Meng, Jianping

This Ph.D. thesis presented here spans two relatively independent topics. The first part, Chapter 2 is self-contained, and is dedicated to studies of new algorithms for power system state estimation. The second part, encompassing the remaining chapters, is dedicated to stability analysis of power system coupled with market dynamics. The first part of this thesis presents improved Newton's methods employing efficient vectorized calculations of higher order derivatives in power system state estimation problems. The improved algorithms are proposed based on an exact Newton's method using the second order terms. By efficiently computing an exact gain matrix, combined with a special optimal multiplier method, the new algorithms show more reliable convergence compared with the existing methods of normal equations, orthogonal decomposition, and Hachtel's sparse tableau. Our methods are able to handle ill-conditioned problems, yet show minimal penalty in computational cost for well-conditioned cases. These claims are illustrated through the standard IEEE 118 and 300 bus test examples. The second part of the thesis focuses on stability analysis of market/power systems. The work presented is motivated by an emerging problem. As the frequency of market based dispatch updates increases, there will inevitably be interaction between the dynamics of markets determining the generator dispatch commands, and the physical response of generators and network interconnections, necessitating the development of stability analysis for such coupled systems. We begin with numeric tests using different market models, with detailed machine/exciter/turbine/governor dynamics, in the New England 39 bus test system. A progression of modeling refinements are introduced, including such non-ideal effects as time delays. Electricity market parameter identification algorithms are also studied based on real time data from the PJM electricity market. Finally our power market model is augmented by optimal power flow constraints, allowing study of the so-called congestion problem. These studies show that understanding of potential modes of instability in such coupled systems is of crucial importance both in designing suitable rules for power markets, and in designing physical generator controls that are complementary to market-based dispatch.

Optimal Real-time Dispatch for Integrated Energy Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Firestone, Ryan Michael

This report describes the development and application of a dispatch optimization algorithm for integrated energy systems (IES) comprised of on-site cogeneration of heat and electricity, energy storage devices, and demand response opportunities. This work is intended to aid commercial and industrial sites in making use of modern computing power and optimization algorithms to make informed, near-optimal decisions under significant uncertainty and complex objective functions. The optimization algorithm uses a finite set of randomly generated future scenarios to approximate the true, stochastic future; constraints are included that prevent solutions to this approximate problem from deviating from solutions to the actual problem.more » The algorithm is then expressed as a mixed integer linear program, to which a powerful commercial solver is applied. A case study of United States Postal Service Processing and Distribution Centers (P&DC) in four cities and under three different electricity tariff structures is conducted to (1) determine the added value of optimal control to a cogeneration system over current, heuristic control strategies; (2) determine the value of limited electric load curtailment opportunities, with and without cogeneration; and (3) determine the trade-off between least-cost and least-carbon operations of a cogeneration system. Key results for the P&DC sites studied include (1) in locations where the average electricity and natural gas prices suggest a marginally profitable cogeneration system, optimal control can add up to 67% to the value of the cogeneration system; optimal control adds less value in locations where cogeneration is more clearly profitable; (2) optimal control under real-time pricing is (a) more complicated than under typical time-of-use tariffs and (b) at times necessary to make cogeneration economic at all; (3) limited electric load curtailment opportunities can be more valuable as a compliment to the cogeneration system than alone; and (4) most of the trade-off between least-cost and least-carbon IES is determined during the system design stage; for the IES system considered, there is little difference between least-cost control and least-carbon control.« less

Modified artificial bee colony algorithm for reactive power optimization

NASA Astrophysics Data System (ADS)

Sulaiman, Noorazliza; Mohamad-Saleh, Junita; Abro, Abdul Ghani

2015-05-01

Bio-inspired algorithms (BIAs) implemented to solve various optimization problems have shown promising results which are very important in this severely complex real-world. Artificial Bee Colony (ABC) algorithm, a kind of BIAs has demonstrated tremendous results as compared to other optimization algorithms. This paper presents a new modified ABC algorithm referred to as JA-ABC3 with the aim to enhance convergence speed and avoid premature convergence. The proposed algorithm has been simulated on ten commonly used benchmarks functions. Its performance has also been compared with other existing ABC variants. To justify its robust applicability, the proposed algorithm has been tested to solve Reactive Power Optimization problem. The results have shown that the proposed algorithm has superior performance to other existing ABC variants e.g. GABC, BABC1, BABC2, BsfABC dan IABC in terms of convergence speed. Furthermore, the proposed algorithm has also demonstrated excellence performance in solving Reactive Power Optimization problem.

Adaptive Fuzzy Hysteresis Band Current Controller for Four-Wire Shunt Active Filter

NASA Astrophysics Data System (ADS)

Hamoudi, F.; Chaghi, A.; Amimeur, H.; Merabet, E.

2008-06-01

This paper presents an adaptive fuzzy hysteresis band current controller for four-wire shunt active power filters to eliminate harmonics and to compensate reactive power in distribution systems in order to keep currents at the point of common coupling sinusoidal and in phase with the corresponding voltage and the cancel neutral current. The conventional hysteresis band known for its robustness and its advantage in current controlled applications is adapted with a fuzzy logic controller to change the bandwidth according to the operating point in order to keep the frequency modulation at tolerable limits. The algorithm used to identify the reference currents is based on the synchronous reference frame theory (dqγ). Finally, simulation results using Matlab/Simulink are given to validate the proposed control.

Robustness of controllability and observability of linear time-varying systems with application to the emergency control of power systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sastry, S. S.; Desoer, C. A.

1980-01-01

Fixed point methods from nonlinear anaysis are used to establish conditions under which the uniform complete controllability of linear time-varying systems is preserved under non-linear perturbations in the state dynamics and the zero-input uniform complete observability of linear time-varying systems is preserved under non-linear perturbation in the state dynamics and output read out map. Algorithms for computing the specific input to steer the perturbed systems from a given initial state to a given final state are also presented. As an application, a very specific emergency control of an interconnected power system is formulated as a steering problem and it ismore » shown that this emergency control is indeed possible in finite time.« less

State transformations and Hamiltonian structures for optimal control in discrete systems

NASA Astrophysics Data System (ADS)

Sieniutycz, S.

2006-04-01

Preserving usual definition of Hamiltonian H as the scalar product of rates and generalized momenta we investigate two basic classes of discrete optimal control processes governed by the difference rather than differential equations for the state transformation. The first class, linear in the time interval θ, secures the constancy of optimal H and satisfies a discrete Hamilton-Jacobi equation. The second class, nonlinear in θ, does not assure the constancy of optimal H and satisfies only a relationship that may be regarded as an equation of Hamilton-Jacobi type. The basic question asked is if and when Hamilton's canonical structures emerge in optimal discrete systems. For a constrained discrete control, general optimization algorithms are derived that constitute powerful theoretical and computational tools when evaluating extremum properties of constrained physical systems. The mathematical basis is Bellman's method of dynamic programming (DP) and its extension in the form of the so-called Carathéodory-Boltyanski (CB) stage optimality criterion which allows a variation of the terminal state that is otherwise fixed in Bellman's method. For systems with unconstrained intervals of the holdup time θ two powerful optimization algorithms are obtained: an unconventional discrete algorithm with a constant H and its counterpart for models nonlinear in θ. We also present the time-interval-constrained extension of the second algorithm. The results are general; namely, one arrives at: discrete canonical equations of Hamilton, maximum principles, and (at the continuous limit of processes with free intervals of time) the classical Hamilton-Jacobi theory, along with basic results of variational calculus. A vast spectrum of applications and an example are briefly discussed with particular attention paid to models nonlinear in the time interval θ.

Suction prevention and physiologic control of continuous flow left ventricular assist devices using intrinsic pump parameters.

PubMed

Wang, Yu; Koenig, Steven C; Slaughter, Mark S; Giridharan, Guruprasad A

2015-01-01

The risk for left ventricular (LV) suction during left ventricular assist devices (LVAD) support has been a clinical concern. Current development efforts suggest LVAD suction prevention and physiologic control algorithms may require chronic implantation of pressure or flow sensors, which can be unreliable because of baseline drift and short lifespan. To overcome this limitation, we designed a sensorless suction prevention and physiologic control (eSPPC) algorithm that only requires LVAD intrinsic parameters (pump speed and power). Two gain-scheduled, proportional-integral controllers maintain a differential pump speed (ΔRPM) above a user-defined threshold to prevent LV suction while maintaining an average reference differential pressure (ΔP) between the LV and aorta. ΔRPM is calculated from noisy pump speed measurements that are low-pass filtered, and ΔP is estimated using an extended Kalman filter. Efficacy and robustness of the eSPPC algorithm were evaluated in silico during simulated rest and exercise test conditions for 1) excessive ΔP setpoint (ES); 2) rapid eightfold increase in pulmonary vascular resistance (PVR); and 3) ES and PVR. Simulated hemodynamic waveforms (LV pressure and volume; aortic pressure and flow) using only intrinsic pump parameters showed the feasibility of our proposed eSPPC algorithm in preventing LV suction for all test conditions.

Design and experiment of vehicular charger AC/DC system based on predictive control algorithm

NASA Astrophysics Data System (ADS)

He, Guangbi; Quan, Shuhai; Lu, Yuzhang

2018-06-01

For the car charging stage rectifier uncontrollable system, this paper proposes a predictive control algorithm of DC/DC converter based on the prediction model, established by the state space average method and its prediction model, obtained by the optimal mathematical description of mathematical calculation, to analysis prediction algorithm by Simulink simulation. The design of the structure of the car charging, at the request of the rated output power and output voltage adjustable control circuit, the first stage is the three-phase uncontrolled rectifier DC voltage Ud through the filter capacitor, after by using double-phase interleaved buck-boost circuit with wide range output voltage required value, analyzing its working principle and the the parameters for the design and selection of components. The analysis of current ripple shows that the double staggered parallel connection has the advantages of reducing the output current ripple and reducing the loss. The simulation experiment of the whole charging circuit is carried out by software, and the result is in line with the design requirements of the system. Finally combining the soft with hardware circuit to achieve charging of the system according to the requirements, experimental platform proved the feasibility and effectiveness of the proposed predictive control algorithm based on the car charging of the system, which is consistent with the simulation results.

Research of PV Power Generation MPPT based on GABP Neural Network

NASA Astrophysics Data System (ADS)

Su, Yu; Lin, Xianfu

2018-05-01

Photovoltaic power generation has become the main research direction of new energy power generation. But high investment and low efficiency of photovoltaic industry arouse concern in some extent. So maximum power point tracking of photovoltaic power generation has been a popular study point. Due to slow response, oscillation at maximum power point and low precision, the algorithm based on genetic algorithm combined with BP neural network are designed detailedly in this paper. And the modeling and simulation are completed by use of MATLAB/SIMULINK. The results show that the algorithm is effective and the maximum power point can be tracked accurately and quickly.

Description of real-time Ada software implementation of a power system monitor for the Space Station Freedom PMAD DC testbed

NASA Technical Reports Server (NTRS)

Ludwig, Kimberly; Mackin, Michael; Wright, Theodore

1991-01-01

The Ada language software development to perform the electrical system monitoring functions for the NASA Lewis Research Center's Power Management and Distribution (PMAD) DC testbed is described. The results of the effort to implement this monitor are presented. The PMAD DC testbed is a reduced-scale prototype of the electrical power system to be used in the Space Station Freedom. The power is controlled by smart switches known as power control components (or switchgear). The power control components are currently coordinated by five Compaq 382/20e computers connected through an 802.4 local area network. One of these computers is designated as the control node with the other four acting as subsidiary controllers. The subsidiary controllers are connected to the power control components with a Mil-Std-1553 network. An operator interface is supplied by adding a sixth computer. The power system monitor algorithm is comprised of several functions including: periodic data acquisition, data smoothing, system performance analysis, and status reporting. Data is collected from the switchgear sensors every 100 milliseconds, then passed through a 2 Hz digital filter. System performance analysis includes power interruption and overcurrent detection. The reporting mechanism notifies an operator of any abnormalities in the system. Once per second, the system monitor provides data to the control node for further processing, such as state estimation. The system monitor required a hardware time interrupt to activate the data acquisition function. The execution time of the code was optimized using an assembly language routine. The routine allows direct vectoring of the processor to Ada language procedures that perform periodic control activities. A summary of the advantages and side effects of this technique are discussed.

A general framework for the manual teleoperation of kinematically redundant space-based manipulators

NASA Astrophysics Data System (ADS)

Dupuis, Erick

This thesis provides a general framework for the manual teleoperation of kinematically redundant space-based manipulators. It is proposed to break down the task of controlling the motion of a redundant manipulator into a sequence of manageable sub-tasks of lower dimension by imposing constraints on the motion of intermediate bodies of the manipulator. This implies that the manipulator then becomes a non-redundant kinematic chain and the operator only controls a reduced number of degrees of freedom at any time. However, by appropriately changing the imposed constraints, the operator can use the full capability of the manipulator throughout the task. Also, by not restricting the point of teleoperation to the end effector but effectively allowing direct control of intermediate bodies of the robot, it is possible to teleoperate a redundant robot of arbitrary kinematic architecture over its entire configuration space in a predictable and natural fashion. It is rigourously proven that this approach will always work for any kinematically redundant serial manipulator regardless of its topology, geometry and of the number of its excess degrees-of-freedom. Furthermore, a methodology is provided for the selection of task and constraint coordinates to ensure the absence of algorithmic rank-deficiencies. Two novel algorithms are provided for the symbolic determination of the rank-deficiency locus of rectangular Jacobian matrices: the Singular Vector Algorithm and the Recursive Sub-Determinant Algorithm. These algorithms are complementary to each other: the former being more computationally efficient and the latter more robust. The application of the methodology to sample cases of varying complexity has demonstrated its power and limitations: It has been shown to be powerful enough to generate complete sets of task/constraint coordinate pairs for realistic examples such as the Space Station Remote Manipulator System and a simplified version of the Special Purpose Dexterous Manipulator.

Finding Minimum-Power Broadcast Trees for Wireless Networks

NASA Technical Reports Server (NTRS)

Arabshahi, Payman; Gray, Andrew; Das, Arindam; El-Sharkawi, Mohamed; Marks, Robert, II

2004-01-01

Some algorithms have been devised for use in a method of constructing tree graphs that represent connections among the nodes of a wireless communication network. These algorithms provide for determining the viability of any given candidate connection tree and for generating an initial set of viable trees that can be used in any of a variety of search algorithms (e.g., a genetic algorithm) to find a tree that enables the network to broadcast from a source node to all other nodes while consuming the minimum amount of total power. The method yields solutions better than those of a prior algorithm known as the broadcast incremental power algorithm, albeit at a slightly greater computational cost.

An Energy-Efficient Underground Localization System Based on Heterogeneous Wireless Networks

PubMed Central

Yuan, Yazhou; Chen, Cailian; Guan, Xinping; Yang, Qiuling

2015-01-01

A precision positioning system with energy efficiency is of great necessity for guaranteeing personnel safety in underground mines. The location information of the miners' should be transmitted to the control center timely and reliably; therefore, a heterogeneous network with the backbone based on high speed Industrial Ethernet is deployed. Since the mobile wireless nodes are working in an irregular tunnel, a specific wireless propagation model cannot fit all situations. In this paper, an underground localization system is designed to enable the adaptation to kinds of harsh tunnel environments, but also to reduce the energy consumption and thus prolong the lifetime of the network. Three key techniques are developed and implemented to improve the system performance, including a step counting algorithm with accelerometers, a power control algorithm and an adaptive packets scheduling scheme. The simulation study and experimental results show the effectiveness of the proposed algorithms and the implementation. PMID:26016918

Implementation and control of a 3 degree-of-freedom, force-reflecting manual controller

NASA Astrophysics Data System (ADS)

Kim, Whee-Kuk; Bevill, Pat; Tesar, Delbert

1991-02-01

Most available manual controllers which are used in bilateral or force-reflecting teleoperator systems can be characterized by their bulky size heavy weight high cost low magnitude of reflecting-force lack of smoothness insufficient transparency and simplified architectures. A compact smooth lightweight portable universal manual controller could provide a markedly improved level of transparency and be able to drive a broad spectrum of slave manipulators. This implies that a single stand-off position could be used for a diverse population of remote systems and that a standard environment for training of operators would result in reduced costs and higher reliability. In the implementation presented in this paper a parallel 3 degree-of-freedom (DOF) spherical structure (for compactness and reduced weight) is combined with high gear-ratio reducers using a force control algorithm to produce a " power steering" effect for enhanced smoothness and transparency. The force control algorithm has the further benefit of minimizing the effect of the system friction and non-linear inertia forces. The fundamental analytical description for the spherical force-reflecting manual controller such as forward position analysis reflecting-force transformation and applied force control algorithm are presented. Also a brief description of the system integration its actual implementation and preliminary test results are presented in the paper.

Towards feasible and effective predictive wavefront control for adaptive optics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poyneer, L A; Veran, J

We have recently proposed Predictive Fourier Control, a computationally efficient and adaptive algorithm for predictive wavefront control that assumes frozen flow turbulence. We summarize refinements to the state-space model that allow operation with arbitrary computational delays and reduce the computational cost of solving for new control. We present initial atmospheric characterization using observations with Gemini North's Altair AO system. These observations, taken over 1 year, indicate that frozen flow is exists, contains substantial power, and is strongly detected 94% of the time.

Voltage stability analysis in the new deregulated environment

NASA Astrophysics Data System (ADS)

Zhu, Tong

Nowadays, a significant portion of the power industry is under deregulation. Under this new circumstance, network security analysis is more critical and more difficult. One of the most important issues in network security analysis is voltage stability analysis. Due to the expected higher utilization of equipment induced by competition in a power market that covers bigger power systems, this issue is increasingly acute after deregulation. In this dissertation, some selected topics of voltage stability analysis are covered. In the first part, after a brief review of general concepts of continuation power flow (CPF), investigations on various matrix analysis techniques to improve the speed of CPF calculation for large systems are reported. Based on these improvements, a new CPF algorithm is proposed. This new method is then tested by an inter-area transaction in a large inter-connected power system. In the second part, the Arnoldi algorithm, the best method to find a few minimum singular values for a large sparse matrix, is introduced into the modal analysis for the first time. This new modal analysis is applied to the estimation of the point of voltage collapse and contingency evaluation in voltage security assessment. Simulations show that the new method is very efficient. In the third part, after transient voltage stability component models are investigated systematically, a novel system model for transient voltage stability analysis, which is a logical-algebraic-differential-difference equation (LADDE), is offered. As an example, TCSC (Thyristor controlled series capacitors) is addressed as a transient voltage stabilizing controller. After a TCSC transient voltage stability model is outlined, a new TCSC controller is proposed to enhance both fault related and load increasing related transient voltage stability. Its ability is proven by the simulation.

Some preliminary results from the NWTC direct-drive, variable-speed test bed

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carlin, P.W.; Fingersh, L.J.

1996-10-01

With the remarkable rise in interest in variable-speed operation of larger wind turbines, it has become important for the National Wind Technology Center (NWTC) to have access to a variable-speed test bed that can be specially instrumented for research. Accordingly, a three-bladed, 10-meter, downwind, Grumman Windstream machine has been equipped with a set of composite blades and a direct-coupled, permanent-magnet, 20 kilowatt generator. This machine and its associated control system and data collection system are discussed. Several variations of a maximum power control algorithm have been installed on the control computer. To provide a baseline for comparison, several constant speedmore » algorithms have also been installed. The present major effort is devoted to daytime, semi-autonomous data collection.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hao, He; Sun, Yannan; Carroll, Thomas E.

We propose a coordination algorithm for cooperative power allocation among a collection of commercial buildings within a campus. We introduced thermal and power models of a typical commercial building Heating, Ventilation, and Air Conditioning (HVAC) system, and utilize model predictive control to characterize their power flexibility. The power allocation problem is formulated as a cooperative game using the Nash Bargaining Solution (NBS) concept, in which buildings collectively maximize the product of their utilities subject to their local flexibility constraints and a total power limit set by the campus coordinator. To solve the optimal allocation problem, a distributed protocol is designedmore » using dual decomposition of the Nash bargaining problem. Numerical simulations are performed to demonstrate the efficacy of our proposed allocation method« less

A fast and accurate frequency estimation algorithm for sinusoidal signal with harmonic components

NASA Astrophysics Data System (ADS)

Hu, Jinghua; Pan, Mengchun; Zeng, Zhidun; Hu, Jiafei; Chen, Dixiang; Tian, Wugang; Zhao, Jianqiang; Du, Qingfa

2016-10-01

Frequency estimation is a fundamental problem in many applications, such as traditional vibration measurement, power system supervision, and microelectromechanical system sensors control. In this paper, a fast and accurate frequency estimation algorithm is proposed to deal with low efficiency problem in traditional methods. The proposed algorithm consists of coarse and fine frequency estimation steps, and we demonstrate that it is more efficient than conventional searching methods to achieve coarse frequency estimation (location peak of FFT amplitude) by applying modified zero-crossing technique. Thus, the proposed estimation algorithm requires less hardware and software sources and can achieve even higher efficiency when the experimental data increase. Experimental results with modulated magnetic signal show that the root mean square error of frequency estimation is below 0.032 Hz with the proposed algorithm, which has lower computational complexity and better global performance than conventional frequency estimation methods.

Intelligent control of PV system on the basis of the fuzzy recurrent neuronet*

NASA Astrophysics Data System (ADS)

Engel, E. A.; Kovalev, I. V.; Engel, N. E.

2016-04-01

This paper presents the fuzzy recurrent neuronet for PV system’s control. Based on the PV system’s state, the fuzzy recurrent neural net tracks the maximum power point under random perturbations. The validity and advantages of the proposed intelligent control of PV system are demonstrated by numerical simulations. The simulation results show that the proposed intelligent control of PV system achieves real-time control speed and competitive performance, as compared to a classical control scheme on the basis of the perturbation & observation algorithm.

A method exploiting direct communication between phasor measurement units for power system wide-area protection and control algorithms.

PubMed

Almas, Muhammad Shoaib; Vanfretti, Luigi

2017-01-01

Synchrophasor measurements from Phasor Measurement Units (PMUs) are the primary sensors used to deploy Wide-Area Monitoring, Protection and Control (WAMPAC) systems. PMUs stream out synchrophasor measurements through the IEEE C37.118.2 protocol using TCP/IP or UDP/IP. The proposed method establishes a direct communication between two PMUs, thus eliminating the requirement of an intermediate phasor data concentrator, data mediator and/or protocol parser and thereby ensuring minimum communication latency without considering communication link delays. This method allows utilizing synchrophasor measurements internally in a PMU to deploy custom protection and control algorithms. These algorithms are deployed using protection logic equations which are supported by all the PMU vendors. Moreover, this method reduces overall equipment cost as the algorithms execute internally in a PMU and therefore does not require any additional controller for their deployment. The proposed method can be utilized for fast prototyping of wide-area measurements based protection and control applications. The proposed method is tested by coupling commercial PMUs as Hardware-in-the-Loop (HIL) with Opal-RT's eMEGAsim Real-Time Simulator (RTS). As illustrative example, anti-islanding protection application is deployed using proposed method and its performance is assessed. The essential points in the method are: •Bypassing intermediate phasor data concentrator or protocol parsers as the synchrophasors are communicated directly between the PMUs (minimizes communication delays).•Wide Area Protection and Control Algorithm is deployed using logic equations in the client PMU, therefore eliminating the requirement for an external hardware controller (cost curtailment)•Effortless means to exploit PMU measurements in an environment familiar to protection engineers.

NREL and DONG Energy Collaboration for Grid Simulator Controls and Testing: Cooperative Research and Development Final Report, CRADA Number CRD-13-527

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gevorgian, Vahan

The National Renewable Energy Laboratory (NREL) and DONG Energy are interested in collaborating for the development of control algorithms, modeling, and grid simulator testing of wind turbine generator systems involving NWTC's advanced Controllable Grid Interface (CGI). NREL and DONG Energy will work together to develop control algorithms, models, test methods, and protocols involving NREL's CGI, as well as appropriate data acquisition systems for grid simulation testing. The CRADA also includes work on joint publication of results achieved from modeling and testing efforts. Further, DONG Energy will send staff to NREL on a long-term basis for collaborative work including modeling andmore » testing. NREL will send staff to DONG Energy on a short-term basis to visit wind power sites and participate in meetings relevant to this collaborative effort. DOE has provided NREL with over 10 years of support in developing custom facilities and capabilities to enable testing of full-scale integrated wind turbine drivetrain systems in accordance with the needs of the US wind industry. NREL currently operates a 2.5MW dynamometer and is in the processes of commissioning a 5MW dynamometer and a grid simulator (referred to as a 'Controllable Grid Interface' or CGI). DONG Energy is the market leader in offshore wind power development, with currently over 1 GW of on- and offshore wind power in operation, and 1.3 GW under construction. DONG Energy has on-going R&D projects involving high voltage DC (HVDC) transmission.« less

A Novel Topology Control Approach to Maintain the Node Degree in Dynamic Wireless Sensor Networks

PubMed Central

Huang, Yuanjiang; Martínez, José-Fernán; Díaz, Vicente Hernández; Sendra, Juana

2014-01-01

Topology control is an important technique to improve the connectivity and the reliability of Wireless Sensor Networks (WSNs) by means of adjusting the communication range of wireless sensor nodes. In this paper, a novel Fuzzy-logic Topology Control (FTC) is proposed to achieve any desired average node degree by adaptively changing communication range, thus improving the network connectivity, which is the main target of FTC. FTC is a fully localized control algorithm, and does not rely on location information of neighbors. Instead of designing membership functions and if-then rules for fuzzy-logic controller, FTC is constructed from the training data set to facilitate the design process. FTC is proved to be accurate, stable and has short settling time. In order to compare it with other representative localized algorithms (NONE, FLSS, k-Neighbor and LTRT), FTC is evaluated through extensive simulations. The simulation results show that: firstly, similar to k-Neighbor algorithm, FTC is the best to achieve the desired average node degree as node density varies; secondly, FTC is comparable to FLSS and k-Neighbor in terms of energy-efficiency, but is better than LTRT and NONE; thirdly, FTC has the lowest average maximum communication range than other algorithms, which indicates that the most energy-consuming node in the network consumes the lowest power. PMID:24608008

Dynamic routing and spectrum assignment based on multilayer virtual topology and ant colony optimization in elastic software-defined optical networks

NASA Astrophysics Data System (ADS)

Wang, Fu; Liu, Bo; Zhang, Lijia; Zhang, Qi; Tian, Qinghua; Tian, Feng; Rao, Lan; Xin, Xiangjun

2017-07-01

Elastic software-defined optical networks greatly improve the flexibility of the optical switching network while it has brought challenges to the routing and spectrum assignment (RSA). A multilayer virtual topology model is proposed to solve RSA problems. Two RSA algorithms based on the virtual topology are proposed, which are the ant colony optimization (ACO) algorithm of minimum consecutiveness loss and the ACO algorithm of maximum spectrum consecutiveness. Due to the computing power of the control layer in the software-defined network, the routing algorithm avoids the frequent link-state information between routers. Based on the effect of the spectrum consecutiveness loss on the pheromone in the ACO, the path and spectrum of the minimal impact on the network are selected for the service request. The proposed algorithms have been compared with other algorithms. The results show that the proposed algorithms can reduce the blocking rate by at least 5% and perform better in spectrum efficiency. Moreover, the proposed algorithms can effectively decrease spectrum fragmentation and enhance available spectrum consecutiveness.

Minimal Increase Network Coding for Dynamic Networks.

PubMed

Zhang, Guoyin; Fan, Xu; Wu, Yanxia

2016-01-01

Because of the mobility, computing power and changeable topology of dynamic networks, it is difficult for random linear network coding (RLNC) in static networks to satisfy the requirements of dynamic networks. To alleviate this problem, a minimal increase network coding (MINC) algorithm is proposed. By identifying the nonzero elements of an encoding vector, it selects blocks to be encoded on the basis of relationship between the nonzero elements that the controls changes in the degrees of the blocks; then, the encoding time is shortened in a dynamic network. The results of simulations show that, compared with existing encoding algorithms, the MINC algorithm provides reduced computational complexity of encoding and an increased probability of delivery.

Minimal Increase Network Coding for Dynamic Networks

PubMed Central

Wu, Yanxia

2016-01-01

Because of the mobility, computing power and changeable topology of dynamic networks, it is difficult for random linear network coding (RLNC) in static networks to satisfy the requirements of dynamic networks. To alleviate this problem, a minimal increase network coding (MINC) algorithm is proposed. By identifying the nonzero elements of an encoding vector, it selects blocks to be encoded on the basis of relationship between the nonzero elements that the controls changes in the degrees of the blocks; then, the encoding time is shortened in a dynamic network. The results of simulations show that, compared with existing encoding algorithms, the MINC algorithm provides reduced computational complexity of encoding and an increased probability of delivery. PMID:26867211

Estimating power capability of aged lithium-ion batteries in presence of communication delays

NASA Astrophysics Data System (ADS)

Fridholm, Björn; Wik, Torsten; Kuusisto, Hannes; Klintberg, Anton

2018-04-01

Efficient control of electrified powertrains requires accurate estimation of the power capability of the battery for the next few seconds into the future. When implemented in a vehicle, the power estimation is part of a control loop that may contain several networked controllers which introduces time delays that may jeopardize stability. In this article, we present and evaluate an adaptive power estimation method that robustly can handle uncertain health status and time delays. A theoretical analysis shows that stability of the closed loop system can be lost if the resistance of the model is under-estimated. Stability can, however, be restored by filtering the estimated power at the expense of slightly reduced bandwidth of the signal. The adaptive algorithm is experimentally validated in lab tests using an aged lithium-ion cell subject to a high power load profile in temperatures from -20 to +25 °C. The upper voltage limit was set to 4.15 V and the lower voltage limit to 2.6 V, where significant non-linearities are occurring and the validity of the model is limited. After an initial transient when the model parameters are adapted, the prediction accuracy is within ± 2 % of the actually available power.

Fundamental Lifetime Mechanisms in Routing Protocols for Wireless Sensor Networks: A Survey and Open Issues

PubMed Central

Eslaminejad, Mohammadreza; Razak, Shukor Abd

2012-01-01

Wireless sensor networks basically consist of low cost sensor nodes which collect data from environment and relay them to a sink, where they will be subsequently processed. Since wireless nodes are severely power-constrained, the major concern is how to conserve the nodes' energy so that network lifetime can be extended significantly. Employing one static sink can rapidly exhaust the energy of sink neighbors. Furthermore, using a non-optimal single path together with a maximum transmission power level may quickly deplete the energy of individual nodes on the route. This all results in unbalanced energy consumption through the sensor field, and hence a negative effect on the network lifetime. In this paper, we present a comprehensive taxonomy of the various mechanisms applied for increasing the network lifetime. These techniques, whether in the routing or cross-layer area, fall within the following types: multi-sink, mobile sink, multi-path, power control and bio-inspired algorithms, depending on the protocol operation. In this taxonomy, special attention has been devoted to the multi-sink, power control and bio-inspired algorithms, which have not yet received much consideration in the literature. Moreover, each class covers a variety of the state-of-the-art protocols, which should provide ideas for potential future works. Finally, we compare these mechanisms and discuss open research issues. PMID:23202008

Fundamental lifetime mechanisms in routing protocols for wireless sensor networks: a survey and open issues.

PubMed

Eslaminejad, Mohammadreza; Razak, Shukor Abd

2012-10-09

Wireless sensor networks basically consist of low cost sensor nodes which collect data from environment and relay them to a sink, where they will be subsequently processed. Since wireless nodes are severely power-constrained, the major concern is how to conserve the nodes' energy so that network lifetime can be extended significantly. Employing one static sink can rapidly exhaust the energy of sink neighbors. Furthermore, using a non-optimal single path together with a maximum transmission power level may quickly deplete the energy of individual nodes on the route. This all results in unbalanced energy consumption through the sensor field, and hence a negative effect on the network lifetime. In this paper, we present a comprehensive taxonomy of the various mechanisms applied for increasing the network lifetime. These techniques, whether in the routing or cross-layer area, fall within the following types: multi-sink, mobile sink, multi-path, power control and bio-inspired algorithms, depending on the protocol operation. In this taxonomy, special attention has been devoted to the multi-sink, power control and bio-inspired algorithms, which have not yet received much consideration in the literature. Moreover, each class covers a variety of the state-of-the-art protocols, which should provide ideas for potential future works. Finally, we compare these mechanisms and discuss open research issues.

Fractional order PID controller for improvement of PMSM speed control in aerospace applications

NASA Astrophysics Data System (ADS)

Saraji, Ali Motalebi; Ghanbari, Mahmood

2014-12-01

Because of the benefits reduced size, cost and maintenance, noise, CO2 emissions and increased control flexibility and precision, to meet these expectations, electrical equipment increasingly utilize in modern aircraft systems and aerospace industry rather than conventional mechanic, hydraulic, and pneumatic power systems. Electric motor drives are capable of converting electrical power to drive actuators, pumps, compressors, and other subsystems at variable speeds. In the past decades, permanent magnet synchronous motor (PMSM) and brushless dc (BLDC) motor were investigated for aerospace applications such as aircraft actuators. In this paper, the fractional-order PID controller is used in the design of speed loop of PMSM speed control system. Having more parameters for tuning fractional order PID controller lead to good performance ratio to integer order. This good performance is shown by comparison fractional order PID controller with the conventional PI and tuned PID controller by Genetic algorithm in MATLAB soft wear.

Detecting Solenoid Valve Deterioration in In-Use Electronic Diesel Fuel Injection Control Systems

PubMed Central

Tsai, Hsun-Heng; Tseng, Chyuan-Yow

2010-01-01

The diesel engine is the main power source for most agricultural vehicles. The control of diesel engine emissions is an important global issue. Fuel injection control systems directly affect fuel efficiency and emissions of diesel engines. Deterioration faults, such as rack deformation, solenoid valve failure, and rack-travel sensor malfunction, are possibly in the fuel injection module of electronic diesel control (EDC) systems. Among these faults, solenoid valve failure is most likely to occur for in-use diesel engines. According to the previous studies, this failure is a result of the wear of the plunger and sleeve, based on a long period of usage, lubricant degradation, or engine overheating. Due to the difficulty in identifying solenoid valve deterioration, this study focuses on developing a sensor identification algorithm that can clearly classify the usability of the solenoid valve, without disassembling the fuel pump of an EDC system for in-use agricultural vehicles. A diagnostic algorithm is proposed, including a feedback controller, a parameter identifier, a linear variable differential transformer (LVDT) sensor, and a neural network classifier. Experimental results show that the proposed algorithm can accurately identify the usability of solenoid valves. PMID:22163597

Iterative Adaptive Dynamic Programming for Solving Unknown Nonlinear Zero-Sum Game Based on Online Data.

PubMed

Zhu, Yuanheng; Zhao, Dongbin; Li, Xiangjun

2017-03-01

H ∞ control is a powerful method to solve the disturbance attenuation problems that occur in some control systems. The design of such controllers relies on solving the zero-sum game (ZSG). But in practical applications, the exact dynamics is mostly unknown. Identification of dynamics also produces errors that are detrimental to the control performance. To overcome this problem, an iterative adaptive dynamic programming algorithm is proposed in this paper to solve the continuous-time, unknown nonlinear ZSG with only online data. A model-free approach to the Hamilton-Jacobi-Isaacs equation is developed based on the policy iteration method. Control and disturbance policies and value are approximated by neural networks (NNs) under the critic-actor-disturber structure. The NN weights are solved by the least-squares method. According to the theoretical analysis, our algorithm is equivalent to a Gauss-Newton method solving an optimization problem, and it converges uniformly to the optimal solution. The online data can also be used repeatedly, which is highly efficient. Simulation results demonstrate its feasibility to solve the unknown nonlinear ZSG. When compared with other algorithms, it saves a significant amount of online measurement time.

Detecting solenoid valve deterioration in in-use electronic diesel fuel injection control systems.

PubMed

Tsai, Hsun-Heng; Tseng, Chyuan-Yow

2010-01-01

The diesel engine is the main power source for most agricultural vehicles. The control of diesel engine emissions is an important global issue. Fuel injection control systems directly affect fuel efficiency and emissions of diesel engines. Deterioration faults, such as rack deformation, solenoid valve failure, and rack-travel sensor malfunction, are possibly in the fuel injection module of electronic diesel control (EDC) systems. Among these faults, solenoid valve failure is most likely to occur for in-use diesel engines. According to the previous studies, this failure is a result of the wear of the plunger and sleeve, based on a long period of usage, lubricant degradation, or engine overheating. Due to the difficulty in identifying solenoid valve deterioration, this study focuses on developing a sensor identification algorithm that can clearly classify the usability of the solenoid valve, without disassembling the fuel pump of an EDC system for in-use agricultural vehicles. A diagnostic algorithm is proposed, including a feedback controller, a parameter identifier, a linear variable differential transformer (LVDT) sensor, and a neural network classifier. Experimental results show that the proposed algorithm can accurately identify the usability of solenoid valves.

Thrust stand evaluation of engine performance improvement algorithms in an F-15 airplane

NASA Technical Reports Server (NTRS)

Conners, Timothy R.

1992-01-01

An investigation is underway to determine the benefits of a new propulsion system optimization algorithm in an F-15 airplane. The performance seeking control (PSC) algorithm optimizes the quasi-steady-state performance of an F100 derivative turbofan engine for several modes of operation. The PSC algorithm uses an onboard software engine model that calculates thrust, stall margin, and other unmeasured variables for use in the optimization. As part of the PSC test program, the F-15 aircraft was operated on a horizontal thrust stand. Thrust was measured with highly accurate load cells. The measured thrust was compared to onboard model estimates and to results from posttest performance programs. Thrust changes using the various PSC modes were recorded. Those results were compared to benefits using the less complex highly integrated digital electronic control (HIDEC) algorithm. The PSC maximum thrust mode increased intermediate power thrust by 10 percent. The PSC engine model did very well at estimating measured thrust and closely followed the transients during optimization. Quantitative results from the evaluation of the algorithms and performance calculation models are included with emphasis on measured thrust results. The report presents a description of the PSC system and a discussion of factors affecting the accuracy of the thrust stand load measurements.

A new maximum power tracking in PV system during partially shaded conditions based on shuffled frog leap algorithm

NASA Astrophysics Data System (ADS)

Sridhar, R.; Jeevananthan, S.; Dash, S. S.; Vishnuram, Pradeep

2017-05-01

Maximum Power Point Trackers (MPPTs) are power electronic conditioners used in photovoltaic (PV) system to ensure that PV structures feed maximum power for the given ambient temperature and sun's irradiation. When the PV panels are shaded by a fraction due to any environment hindrances then, conventional MPPT trackers may fail in tracking the appropriate peak power as there will be multi power peaks. In this work, a shuffled frog leap algorithm (SFLA) is proposed and it successfully identifies the global maximum power point among other local maxima. The SFLA MPPT is compared with a well-entrenched conventional perturb and observe (P&O) MPPT algorithm and a global search particle swarm optimisation (PSO) MPPT. The simulation results reveal that the proposed algorithm is highly advantageous than P&O, as it tracks nearly 30% more power for a given shading pattern. The credible nature of the proposed SFLA is ensured when it outplays PSO MPPT in convergence. The whole system is realised in MATLAB/Simulink environment.

Advanced illumination control algorithm for medical endoscopy applications

NASA Astrophysics Data System (ADS)

Sousa, Ricardo M.; Wäny, Martin; Santos, Pedro; Morgado-Dias, F.

2015-05-01

CMOS image sensor manufacturer, AWAIBA, is providing the world's smallest digital camera modules to the world market for minimally invasive surgery and one time use endoscopic equipment. Based on the world's smallest digital camera head and the evaluation board provided to it, the aim of this paper is to demonstrate an advanced fast response dynamic control algorithm of the illumination LED source coupled to the camera head, over the LED drivers embedded on the evaluation board. Cost efficient and small size endoscopic camera modules nowadays embed minimal size image sensors capable of not only adjusting gain and exposure time but also LED illumination with adjustable illumination power. The LED illumination power has to be dynamically adjusted while navigating the endoscope over changing illumination conditions of several orders of magnitude within fractions of the second to guarantee a smooth viewing experience. The algorithm is centered on the pixel analysis of selected ROIs enabling it to dynamically adjust the illumination intensity based on the measured pixel saturation level. The control core was developed in VHDL and tested in a laboratory environment over changing light conditions. The obtained results show that it is capable of achieving correction speeds under 1 s while maintaining a static error below 3% relative to the total number of pixels on the image. The result of this work will allow the integration of millimeter sized high brightness LED sources on minimal form factor cameras enabling its use in endoscopic surgical robotic or micro invasive surgery.

Research on fast algorithm of small UAV navigation in non-linear matrix reductionism method

NASA Astrophysics Data System (ADS)

Zhang, Xiao; Fang, Jiancheng; Sheng, Wei; Cao, Juanjuan

2008-10-01

The low Reynolds numbers of small UAV will result in unfavorable aerodynamic conditions to support controlled flight. And as operated near ground, the small UAV will be affected seriously by low-frequency interference caused by atmospheric disturbance. Therefore, the GNC system needs high frequency of attitude estimation and control to realize the steady of the UAV. In company with the dimensional of small UAV dwindling away, its GNC system is more and more taken embedded designing technology to reach the purpose of compactness, light weight and low power consumption. At the same time, the operational capability of GNC system also gets limit in a certain extent. Therefore, a kind of high speed navigation algorithm design becomes the imminence demand of GNC system. Aiming at such requirement, a kind of non-linearity matrix reduction approach is adopted in this paper to create a new high speed navigation algorithm which holds the radius of meridian circle and prime vertical circle as constant and linearizes the position matrix calculation formulae of navigation equation. Compared with normal navigation algorithm, this high speed navigation algorithm decreases 17.3% operand. Within small UAV"s mission radius (20km), the accuracy of position error is less than 0.13m. The results of semi-physical experiments and small UAV's auto pilot testing proved that this algorithm can realize high frequency attitude estimation and control. It will avoid low-frequency interference caused by atmospheric disturbance properly.

A grid-connected single-phase photovoltaic micro inverter

NASA Astrophysics Data System (ADS)

Wen, X. Y.; Lin, P. J.; Chen, Z. C.; Wu, L. J.; Cheng, S. Y.

2017-11-01

In this paper, the topology of a single-phase grid-connected photovoltaic (PV) micro-inverter is proposed. The PV micro-inverter consists of DC-DC stage with high voltage gain boost and DC-AC conversion stage. In the first stage, we apply the active clamp circuit and two voltage multipliers to achieve soft switching technology and high voltage gain. In addition, the flower pollination algorithm (FPA) is employed for the maximum power point tracking (MPPT) in the PV module in this stage. The second stage cascades a H-bridge inverter and LCL filter. To feed high quality sinusoidal power into the grid, the software phase lock, outer voltage loop and inner current loop control method are adopted as the control strategy. The performance of the proposed topology is tested by Matlab/Simulink. A PV module with maximum power 300W and maximum power point voltage 40V is applied as the input source. The simulation results indicate that the proposed topology and the control strategy are feasible.

Artificial Intelligence Based Control Power Optimization on Tailless Aircraft. [ARMD Seedling Fund Phase I

NASA Technical Reports Server (NTRS)

Gern, Frank; Vicroy, Dan D.; Mulani, Sameer B.; Chhabra, Rupanshi; Kapania, Rakesh K.; Schetz, Joseph A.; Brown, Derrell; Princen, Norman H.

2014-01-01

Traditional methods of control allocation optimization have shown difficulties in exploiting the full potential of controlling large arrays of control devices on innovative air vehicles. Artificial neutral networks are inspired by biological nervous systems and neurocomputing has successfully been applied to a variety of complex optimization problems. This project investigates the potential of applying neurocomputing to the control allocation optimization problem of Hybrid Wing Body (HWB) aircraft concepts to minimize control power, hinge moments, and actuator forces, while keeping system weights within acceptable limits. The main objective of this project is to develop a proof-of-concept process suitable to demonstrate the potential of using neurocomputing for optimizing actuation power for aircraft featuring multiple independently actuated control surfaces. A Nastran aeroservoelastic finite element model is used to generate a learning database of hinge moment and actuation power characteristics for an array of flight conditions and control surface deflections. An artificial neural network incorporating a genetic algorithm then uses this training data to perform control allocation optimization for the investigated aircraft configuration. The phase I project showed that optimization results for the sum of required hinge moments are improved by more than 12% over the best Nastran solution by using the neural network optimization process.

Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems

PubMed Central

Wang, Hailong; Sun, Yuqiu; Su, Qinghua; Xia, Xuewen

2018-01-01

The backtracking search optimization algorithm (BSA) is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA) to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F) is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive ε-constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed. PMID:29666635

Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning.

PubMed

Liu, Weirong; Zhuang, Peng; Liang, Hao; Peng, Jun; Huang, Zhiwu; Weirong Liu; Peng Zhuang; Hao Liang; Jun Peng; Zhiwu Huang; Liu, Weirong; Liang, Hao; Peng, Jun; Zhuang, Peng; Huang, Zhiwu

2018-06-01

Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.

Turbofan engine demonstration of sensor failure detection

NASA Technical Reports Server (NTRS)

Merrill, Walter C.; Delaat, John C.; Abdelwahab, Mahmood

1991-01-01

In the paper, the results of a full-scale engine demonstration of a sensor failure detection algorithm are presented. The algorithm detects, isolates, and accommodates sensor failures using analytical redundancy. The experimental hardware, including the F100 engine, is described. Demonstration results were obtained over a large portion of a typical flight envelope for the F100 engine. They include both subsonic and supersonic conditions at both medium and full, nonafter burning, power. Estimated accuracy, minimum detectable levels of sensor failures, and failure accommodation performance for an F100 turbofan engine control system are discussed.

Evaluation of the Boson Chemiluminescence Immunoassay as a First-Line Screening Test in the ECDC Algorithm for Syphilis Serodiagnosis in a Population with a High Prevalence of Syphilis

PubMed Central

Qiu, Xin-Hui; Zhang, Ya-Feng; Chen, Yu-Yan; Zhang, Qiao; Chen, Fu-Yi; Liu, Long; Fan, Jin-Yi; Gao, Kun; Zhu, Xiao-Zhen; Zheng, Wei-Hong; Zhang, Hui-Lin; Lin, Li-Rong; Liu, Li-Li; Tong, Man-Li; Zhang, Chang-Gong

2015-01-01

We developed a new Boson chemiluminescence immunoassay (CIA) and evaluated its application with cross-sectional analyses. Our results indicated that the Boson CIA demonstrated strong discriminatory power in diagnosing syphilis and that it can be used as a first-line screening test for syphilis serodiagnosis using the European Centre for Disease Prevention and Control algorithm or as a confirmatory test when combined with a patient's clinical history. PMID:25631792

Phase locking of a seven-channel continuous wave fibre laser system by a stochastic parallel gradient algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Volkov, M V; Garanin, S G; Dolgopolov, Yu V

2014-11-30

A seven-channel fibre laser system operated by the master oscillator – multichannel power amplifier scheme is the phase locked using a stochastic parallel gradient algorithm. The phase modulators on lithium niobate crystals are controlled by a multichannel electronic unit with the microcontroller processing signals in real time. The dynamic phase locking of the laser system with the bandwidth of 14 kHz is demonstrated, the time of phasing is 3 – 4 ms. (fibre and integrated-optical structures)

A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm.

PubMed

Dethier, Julie; Nuyujukian, Paul; Eliasmith, Chris; Stewart, Terry; Elassaad, Shauki A; Shenoy, Krishna V; Boahen, Kwabena

2011-01-01

Motor prostheses aim to restore function to disabled patients. Despite compelling proof of concept systems, barriers to clinical translation remain. One challenge is to develop a low-power, fully-implantable system that dissipates only minimal power so as not to damage tissue. To this end, we implemented a Kalman-filter based decoder via a spiking neural network (SNN) and tested it in brain-machine interface (BMI) experiments with a rhesus monkey. The Kalman filter was trained to predict the arm's velocity and mapped on to the SNN using the Neural Engineering Framework (NEF). A 2,000-neuron embedded Matlab SNN implementation runs in real-time and its closed-loop performance is quite comparable to that of the standard Kalman filter. The success of this closed-loop decoder holds promise for hardware SNN implementations of statistical signal processing algorithms on neuromorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses.

Prehensile control of a hand prosthesis by a microcontroller.

PubMed

Chappell, P H; Kyberd, P J

1991-09-01

The functional replacement of a natural hand and wrist is usually achieved by a split hook or an electrically powered and myoelectrically controlled artificial hand with one degree of freedom. In contrast to the commercial devices, this paper describes an experimental hand with four electric motors, nineteen sensors, and control algorithms which are written for a microcontroller. The hand significantly improves the prehension capabilities of an artificial device and leads to a design which is easily controlled by a user as it mimics the control system of the natural hand.

First Trial of Real-time Poloidal Beta Control in KSTAR

NASA Astrophysics Data System (ADS)

Han, Hyunsun; Hahn, S. H.; Bak, J. G.; Walker, M. L.; Woo, M. H.; Kim, J. S.; Kim, Y. J.; Bae, Y. S.; KSTAR Team

2014-10-01

Sustaining the plasma in a stable and a high performance condition is one of the important control issues for future steady state tokamaks. In the 2014 KSTAR campaign, we have developed a real-time poloidal beta (βp) control technique and carried out preliminary experiments to identify its feasibility. In the control system, the βp is calculated in real time using the measured diamagnetic loop signal (DLM03) with coil pickup corrections, and compared with the target value leading to the change of the neutral beam (NB) heating power using a feedback PID control algorithm. To match the required power of NB which is operated with constant voltage, the duty cycles of the modulation were adjusted as the ratio of the required power to the maximum achievable one. This paper will present the overall procedures of the βp control, the βp estimation process implemented in the plasma control system, and the analysis on the preliminary experimental results. This work is supported by the KSTAR research project funded by the Ministry of Science, ICT & Future Planning of Korea.

Seismic isolation device having charging function by a transducer

NASA Astrophysics Data System (ADS)

Yamaguchi, Takashi; Miura, Nanako; Takahashi, Masaki

2016-04-01

In late years, many base isolated structures are planned as the seismic design, because they suppress vibration response significantly against large earthquake. To achieve greater safety, semi-active or active vibration control system is installed in the structures as earthquake countermeasures. Semi-active and active vibration control systems are more effective than passive vibration control system to large earthquake in terms of vibration reduction. However semi-active and active vibration control system cannot operate as required when external power supply is cut off. To solve the problem of energy consumption, we propose a self-powered active seismic isolation floor which achieve active control system using regenerated vibration energy. This device doesn't require external energy to produce control force. The purpose of this study is to propose the seismic isolation device having charging function and to optimize the control system and passive elements such as spring coefficients and damping coefficients using genetic algorithm. As a result, optimized model shows better performance in terms of vibration reduction and electric power regeneration than the previous model. At the end of this paper, the experimental specimen of the proposed isolation device is shown.

A robust approach to chance constrained optimal power flow with renewable generation

DOE PAGES

Lubin, Miles; Dvorkin, Yury; Backhaus, Scott N.

2016-09-01

Optimal Power Flow (OPF) dispatches controllable generation at minimum cost subject to operational constraints on generation and transmission assets. The uncertainty and variability of intermittent renewable generation is challenging current deterministic OPF approaches. Recent formulations of OPF use chance constraints to limit the risk from renewable generation uncertainty, however, these new approaches typically assume the probability distributions which characterize the uncertainty and variability are known exactly. We formulate a robust chance constrained (RCC) OPF that accounts for uncertainty in the parameters of these probability distributions by allowing them to be within an uncertainty set. The RCC OPF is solved usingmore » a cutting-plane algorithm that scales to large power systems. We demonstrate the RRC OPF on a modified model of the Bonneville Power Administration network, which includes 2209 buses and 176 controllable generators. In conclusion, deterministic, chance constrained (CC), and RCC OPF formulations are compared using several metrics including cost of generation, area control error, ramping of controllable generators, and occurrence of transmission line overloads as well as the respective computational performance.« less

Feedback control system based on a remote operated PID controller implemented using mbed NXP LPC1768 development board

NASA Astrophysics Data System (ADS)

Pricop, Emil; Zamfir, Florin; Paraschiv, Nicolae

2015-11-01

Process control is a challenging research topic for both academia and industry for a long time. Controllers evolved from the classical SISO approach to modern fuzzy or neuro-fuzzy embedded devices with networking capabilities, however PID algorithms are still used in the most industrial control loops. In this paper, we focus on the implementation of a PID controller using mbed NXP LPC1768 development board. This board integrates a powerful ARM Cortex- M3 core and has networking capabilities. The implemented controller can be remotely operated by using an Internet connection and a standard Web browser. The main advantages of the proposed embedded system are customizability, easy operation and very low power consumption. The experimental results obtained by using a simulated process are analysed and shows that the implementation can be done with success in industrial applications.

Real-time control of focused ultrasound heating based on rapid MR thermometry.

PubMed

Vimeux, F C; De Zwart, J A; Palussiére, J; Fawaz, R; Delalande, C; Canioni, P; Grenier, N; Moonen, C T

1999-03-01

Real-time control of the heating procedure is essential for hyperthermia applications of focused ultrasound (FUS). The objective of this study is to demonstrate the feasibility of MRI-controlled FUS. An automatic control system was developed using a dedicated interface between the MR system control computer and the FUS wave generator. Two algorithms were used to regulate FUS power to maintain the focal point temperature at a desired level. Automatic control of FUS power level was demonstrated ex vivo at three target temperature levels (increase of 5 degrees C, 10 degrees C, and 30 degrees C above room temperature) during 30-minute hyperthermic periods. Preliminary in vivo results on rat leg muscle confirm that necrosis estimate, calculated on-line during FUS sonication, allows prediction of tissue damage. CONCLUSIONS. The feasibility of fully automatic FUS control based on MRI thermometry has been demonstrated.

Hybrid Stochastic Search Technique based Suboptimal AGC Regulator Design for Power System using Constrained Feedback Control Strategy

NASA Astrophysics Data System (ADS)

Ibraheem, Omveer, Hasan, N.

2010-10-01

A new hybrid stochastic search technique is proposed to design of suboptimal AGC regulator for a two area interconnected non reheat thermal power system incorporating DC link in parallel with AC tie-line. In this technique, we are proposing the hybrid form of Genetic Algorithm (GA) and simulated annealing (SA) based regulator. GASA has been successfully applied to constrained feedback control problems where other PI based techniques have often failed. The main idea in this scheme is to seek a feasible PI based suboptimal solution at each sampling time. The feasible solution decreases the cost function rather than minimizing the cost function.

A multi target approach to control chemical reactions in their inhomogeneous solvent environment

NASA Astrophysics Data System (ADS)

Keefer, Daniel; Thallmair, Sebastian; Zauleck, Julius P. P.; de Vivie-Riedle, Regina

2015-12-01

Shaped laser pulses offer a powerful tool to manipulate molecular quantum systems. Their application to chemical reactions in solution is a promising concept to redesign chemical synthesis. Along this road, theoretical developments to include the solvent surrounding are necessary. An appropriate theoretical treatment is helpful to understand the underlying mechanisms. In our approach we simulate the solvent by randomly selected snapshots from molecular dynamics trajectories. We use multi target optimal control theory to optimize pulses for the various arrangements of explicit solvent molecules simultaneously. This constitutes a major challenge for the control algorithm, as the solvent configurations introduce a large inhomogeneity to the potential surfaces. We investigate how the algorithm handles the new challenges and how well the controllability of the system is preserved with increasing complexity. Additionally, we introduce a way to statistically estimate the efficiency of the optimized laser pulses in the complete thermodynamical ensemble.

Power control of SAFE reactor using fuzzy logic

NASA Astrophysics Data System (ADS)

Irvine, Claude

2002-01-01

Controlling the 100 kW SAFE (Safe Affordable Fission Engine) reactor consists of design and implementation of a fuzzy logic process control system to regulate dynamic variables related to nuclear system power. The first phase of development concentrates primarily on system power startup and regulation, maintaining core temperature equilibrium, and power profile matching. This paper discusses the experimental work performed in those areas. Nuclear core power from the fuel elements is simulated using resistive heating elements while heat rejection is processed by a series of heat pipes. Both axial and radial nuclear power distributions are determined from neuronic modeling codes. The axial temperature profile of the simulated core is matched to the nuclear power profile by varying the resistance of the heating elements. The SAFE model establishes radial temperature profile equivalence by establishing 32 control zones as the nodal coordinates. Control features also allow for slow warm up, since complete shutoff can occur in the heat pipes if heat-source temperatures drop/rise below a certain minimum value, depending on the specific fluid and gas combination in the heat pipe. The entire system is expected to be self-adaptive, i.e., capable of responding to long-range changes in the space environment. Particular attention in the development of the fuzzy logic algorithm shall ensure that the system process remains at set point, virtually eliminating overshoot on start-up and during in-process disturbances. The controller design will withstand harsh environments and applications where it might come in contact with water, corrosive chemicals, radiation fields, etc. .

A novel power efficient location-based cooperative routing with transmission power-upper-limit for wireless sensor networks.

PubMed

Shi, Juanfei; Calveras, Anna; Cheng, Ye; Liu, Kai

2013-05-15

The extensive usage of wireless sensor networks (WSNs) has led to the development of many power- and energy-efficient routing protocols. Cooperative routing in WSNs can improve performance in these types of networks. In this paper we discuss the existing proposals and we propose a routing algorithm for wireless sensor networks called Power Efficient Location-based Cooperative Routing with Transmission Power-upper-limit (PELCR-TP). The algorithm is based on the principle of minimum link power and aims to take advantage of nodes cooperation to make the link work well in WSNs with a low transmission power. In the proposed scheme, with a determined transmission power upper limit, nodes find the most appropriate next nodes and single-relay nodes with the proposed algorithm. Moreover, this proposal subtly avoids non-working nodes, because we add a Bad nodes Avoidance Strategy (BAS). Simulation results show that the proposed algorithm with BAS can significantly improve the performance in reducing the overall link power, enhancing the transmission success rate and decreasing the retransmission rate.

A Novel Power Efficient Location-Based Cooperative Routing with Transmission Power-Upper-Limit for Wireless Sensor Networks

PubMed Central

Shi, Juanfei; Calveras, Anna; Cheng, Ye; Liu, Kai

2013-01-01

The extensive usage of wireless sensor networks (WSNs) has led to the development of many power- and energy-efficient routing protocols. Cooperative routing in WSNs can improve performance in these types of networks. In this paper we discuss the existing proposals and we propose a routing algorithm for wireless sensor networks called Power Efficient Location-based Cooperative Routing with Transmission Power-upper-limit (PELCR-TP). The algorithm is based on the principle of minimum link power and aims to take advantage of nodes cooperation to make the link work well in WSNs with a low transmission power. In the proposed scheme, with a determined transmission power upper limit, nodes find the most appropriate next nodes and single-relay nodes with the proposed algorithm. Moreover, this proposal subtly avoids non-working nodes, because we add a Bad nodes Avoidance Strategy (BAS). Simulation results show that the proposed algorithm with BAS can significantly improve the performance in reducing the overall link power, enhancing the transmission success rate and decreasing the retransmission rate. PMID:23676625

An examination of loads and responses of a wind turbine undergoing variable-speed operation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wright, A.D.; Buhl, M.L. Jr.; Bir, G.S.

1996-11-01

The National Renewable Energy Laboratory has recently developed the ability to predict turbine loads and responses for machines undergoing variable-speed operation. The wind industry has debated the potential benefits of operating wind turbine sat variable speeds for some time. Turbine system dynamic responses (structural response, resonance, and component interactions) are an important consideration for variable-speed operation of wind turbines. The authors have implemented simple, variable-speed control algorithms for both the FAST and ADAMS dynamics codes. The control algorithm is a simple one, allowing the turbine to track the optimum power coefficient (C{sub p}). The objective of this paper is tomore » show turbine loads and responses for a particular two-bladed, teetering-hub, downwind turbine undergoing variable-speed operation. The authors examined the response of the machine to various turbulent wind inflow conditions. In addition, they compare the structural responses under fixed-speed and variable-speed operation. For this paper, they restrict their comparisons to those wind-speed ranges for which limiting power by some additional control strategy (blade pitch or aileron control, for example) is not necessary. The objective here is to develop a basic understanding of the differences in loads and responses between the fixed-speed and variable-speed operation of this wind turbine configuration.« less

Voltage control on a train system

DOEpatents

Gordon, Susanna P.; Evans, John A.

2004-01-20

The present invention provides methods for preventing low train voltages and managing interference, thereby improving the efficiency, reliability, and passenger comfort associated with commuter trains. An algorithm implementing neural network technology is used to predict low voltages before they occur. Once voltages are predicted, then multiple trains can be controlled to prevent low voltage events. Further, algorithms for managing inference are presented in the present invention. Different types of interference problems are addressed in the present invention such as "Interference During Acceleration", "Interference Near Station Stops", and "Interference During Delay Recovery." Managing such interference avoids unnecessary brake/acceleration cycles during acceleration, immediately before station stops, and after substantial delays. Algorithms are demonstrated to avoid oscillatory brake/acceleration cycles due to interference and to smooth the trajectories of closely following trains. This is achieved by maintaining sufficient following distances to avoid unnecessary braking/accelerating. These methods generate smooth train trajectories, making for a more comfortable ride, and improve train motor reliability by avoiding unnecessary mode-changes between propulsion and braking. These algorithms can also have a favorable impact on traction power system requirements and energy consumption.

A Bearingless Switched-Reluctance Motor for High Specific Power Applications

NASA Technical Reports Server (NTRS)

Choi, Benjamin B.; Siebert, Mark

2006-01-01

A 12-8 switched-reluctance motor (SRM) is studied in bearingless (or self-levitated) operation with coil currents limited to the linear region to avoid magnetic saturation. The required motoring and levitating currents are summed and go into a single motor coil per pole to obtain the highest power output of the motor by having more space for motor coil winding. Two controllers are investigated for the bearingless SRM operation. First, a model-based controller using the radial force, which is adjusted by a factor derived from finite element analysis, is presented. Then a simple and practical observation-based controller using a PD (proportional-derivative) control algorithm is presented. Both controllers were experimentally demonstrated to 6500 rpm. This paper reports the initial efforts toward eventual self levitation of a SRM operating into strong magnetic core saturation at liquid nitrogen temperature.

An investigation of quasi-inertial attitude control for a solar power satellite

NASA Technical Reports Server (NTRS)

Juang, J.-N.; Wang, S. J.

1982-01-01

An efficient means, a quasi-inertial attitude mode, is developed for maintaining the normal solar orientation of a space satellite for power collection in a synchronous orbit. Formulae are presented which establish the basic parametric properties for ideal quasi-inertial attitude and phasing. An active control system is necessary to compensate for the energy loss since energy dissipation in widely oscillating flexible bodies produces an instability of the quasi-inertial attitude in the sense that the spacecraft will tumble at the orbit rate. A fixed terminal time and state optimal control problem is formulated and an algorithm for determining the optimal control as a means for the periodical attitude and phase compensation is developed. The vehicle orientation affected by internal disturbance (structural flexibility) and external disturbances (e.g., drag forces) is maintained by a specialized controller design.

A distributed control approach for power and energy management in a notional shipboard power system

NASA Astrophysics Data System (ADS)

Shen, Qunying

The main goal of this thesis is to present a power control module (PCON) based approach for power and energy management and to examine its control capability in shipboard power system (SPS). The proposed control scheme is implemented in a notional medium voltage direct current (MVDC) integrated power system (IPS) for electric ship. To realize the control functions such as ship mode selection, generator launch schedule, blackout monitoring, and fault ride-through, a PCON based distributed power and energy management system (PEMS) is developed. The control scheme is proposed as two-layer hierarchical architecture with system level on the top as the supervisory control and zonal level on the bottom as the decentralized control, which is based on the zonal distribution characteristic of the notional MVDC IPS that was proposed as one of the approaches for Next Generation Integrated Power System (NGIPS) by Norbert Doerry. Several types of modules with different functionalities are used to derive the control scheme in detail for the notional MVDC IPS. Those modules include the power generation module (PGM) that controls the function of generators, the power conversion module (PCM) that controls the functions of DC/DC or DC/AC converters, etc. Among them, the power control module (PCON) plays a critical role in the PEMS. It is the core of the control process. PCONs in the PEMS interact with all the other modules, such as power propulsion module (PPM), energy storage module (ESM), load shedding module (LSHED), and human machine interface (HMI) to realize the control algorithm in PEMS. The proposed control scheme is implemented in real time using the real time digital simulator (RTDS) to verify its validity. To achieve this, a system level energy storage module (SESM) and a zonal level energy storage module (ZESM) are developed in RTDS to cooperate with PCONs to realize the control functionalities. In addition, a load shedding module which takes into account the reliability of power supply (in terms of quality of service) is developed. This module can supply uninterruptible power to the mission critical loads. In addition, a multi-agent system (MAS) based framework is proposed to implement the PCON based PEMS through a hardware setup that is composed of MAMBA boards and FPGA interface. Agents are implemented using Java Agent DEvelopment Framework (JADE). Various test scenarios were tested to validate the approach.

Solution to automatic generation control problem using firefly algorithm optimized I(λ)D(µ) controller.

PubMed

Debbarma, Sanjoy; Saikia, Lalit Chandra; Sinha, Nidul

2014-03-01

Present work focused on automatic generation control (AGC) of a three unequal area thermal systems considering reheat turbines and appropriate generation rate constraints (GRC). A fractional order (FO) controller named as I(λ)D(µ) controller based on crone approximation is proposed for the first time as an appropriate technique to solve the multi-area AGC problem in power systems. A recently developed metaheuristic algorithm known as firefly algorithm (FA) is used for the simultaneous optimization of the gains and other parameters such as order of integrator (λ) and differentiator (μ) of I(λ)D(µ) controller and governor speed regulation parameters (R). The dynamic responses corresponding to optimized I(λ)D(µ) controller gains, λ, μ, and R are compared with that of classical integer order (IO) controllers such as I, PI and PID controllers. Simulation results show that the proposed I(λ)D(µ) controller provides more improved dynamic responses and outperforms the IO based classical controllers. Further, sensitivity analysis confirms the robustness of the so optimized I(λ)D(µ) controller to wide changes in system loading conditions and size and position of SLP. Proposed controller is also found to have performed well as compared to IO based controllers when SLP takes place simultaneously in any two areas or all the areas. Robustness of the proposed I(λ)D(µ) controller is also tested against system parameter variations. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

A maximum power point prediction method for group control of photovoltaic water pumping systems based on parameter identification

NASA Astrophysics Data System (ADS)

Chen, B.; Su, J. H.; Guo, L.; Chen, J.

2017-06-01

This paper puts forward a maximum power estimation method based on the photovoltaic array (PVA) model to solve the optimization problems about group control of the PV water pumping systems (PVWPS) at the maximum power point (MPP). This method uses the improved genetic algorithm (GA) for model parameters estimation and identification in view of multi P-V characteristic curves of a PVA model, and then corrects the identification results through least square method. On this basis, the irradiation level and operating temperature under any condition are able to estimate so an accurate PVA model is established and the MPP none-disturbance estimation is achieved. The simulation adopts the proposed GA to determine parameters, and the results verify the accuracy and practicability of the methods.

Research on Novel Algorithms for Smart Grid Reliability Assessment and Economic Dispatch

NASA Astrophysics Data System (ADS)

Luo, Wenjin

In this dissertation, several studies of electric power system reliability and economy assessment methods are presented. To be more precise, several algorithms in evaluating power system reliability and economy are studied. Furthermore, two novel algorithms are applied to this field and their simulation results are compared with conventional results. As the electrical power system develops towards extra high voltage, remote distance, large capacity and regional networking, the application of a number of new technique equipments and the electric market system have be gradually established, and the results caused by power cut has become more and more serious. The electrical power system needs the highest possible reliability due to its complication and security. In this dissertation the Boolean logic Driven Markov Process (BDMP) method is studied and applied to evaluate power system reliability. This approach has several benefits. It allows complex dynamic models to be defined, while maintaining its easy readability as conventional methods. This method has been applied to evaluate IEEE reliability test system. The simulation results obtained are close to IEEE experimental data which means that it could be used for future study of the system reliability. Besides reliability, modern power system is expected to be more economic. This dissertation presents a novel evolutionary algorithm named as quantum evolutionary membrane algorithm (QEPS), which combines the concept and theory of quantum-inspired evolutionary algorithm and membrane computation, to solve the economic dispatch problem in renewable power system with on land and offshore wind farms. The case derived from real data is used for simulation tests. Another conventional evolutionary algorithm is also used to solve the same problem for comparison. The experimental results show that the proposed method is quick and accurate to obtain the optimal solution which is the minimum cost for electricity supplied by wind farm system.

High-Performance AC Power Source by Applying Robust Stability Control Technology for Precision Material Machining

NASA Astrophysics Data System (ADS)

Chang, En-Chih

2018-02-01

This paper presents a high-performance AC power source by applying robust stability control technology for precision material machining (PMM). The proposed technology associates the benefits of finite-time convergent sliding function (FTCSF) and firefly optimization algorithm (FOA). The FTCSF maintains the robustness of conventional sliding mode, and simultaneously speeds up the convergence speed of the system state. Unfortunately, when a highly nonlinear loading is applied, the chatter will occur. The chatter results in high total harmonic distortion (THD) output voltage of AC power source, and even deteriorates the stability of PMM. The FOA is therefore used to remove the chatter, and the FTCSF still preserves finite system-state convergence time. By combining FTCSF with FOA, the AC power source of PMM can yield good steady-state and transient performance. Experimental results are performed in support of the proposed technology.

Market-Based Coordination of Thermostatically Controlled Loads—Part II: Unknown Parameters and Case Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Sen; Zhang, Wei; Lian, Jianming

This two-part paper considers the coordination of a population of Thermostatically Controlled Loads (TCLs) with unknown parameters to achieve group objectives. The problem involves designing the bidding and market clearing strategy to motivate self-interested users to realize efficient energy allocation subject to a peak power constraint. The companion paper (Part I) formulates the problem and proposes a load coordination framework using the mechanism design approach. To address the unknown parameters, Part II of this paper presents a joint state and parameter estimation framework based on the expectation maximization algorithm. The overall framework is then validated using real-world weather data andmore » price data, and is compared with other approaches in terms of aggregated power response. Simulation results indicate that our coordination framework can effectively improve the efficiency of the power grid operations and reduce power congestion at key times.« less

Online adaptive neural control of a robotic lower limb prosthesis

NASA Astrophysics Data System (ADS)

Spanias, J. A.; Simon, A. M.; Finucane, S. B.; Perreault, E. J.; Hargrove, L. J.

2018-02-01

Objective. The purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputee’s neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. Approach. We present a powered lower limb prosthesis that was configured to acquire the user’s neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user’s intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user’s model of neural data during ambulation. Main results. Our proposed algorithm accurately and consistently identified the user’s intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm—with a 6.66% [3.16%] relative decrease in error rate. Significance. This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user’s intent with low error rates.

Detection of Gait Modes Using an Artificial Neural Network during Walking with a Powered Ankle-Foot Orthosis

PubMed Central

2016-01-01

This paper presents an algorithm, for use with a Portable Powered Ankle-Foot Orthosis (i.e., PPAFO) that can automatically detect changes in gait modes (level ground, ascent and descent of stairs or ramps), thus allowing for appropriate ankle actuation control during swing phase. An artificial neural network (ANN) algorithm used input signals from an inertial measurement unit and foot switches, that is, vertical velocity and segment angle of the foot. Output from the ANN was filtered and adjusted to generate a final data set used to classify different gait modes. Five healthy male subjects walked with the PPAFO on the right leg for two test scenarios (walking over level ground and up and down stairs or a ramp; three trials per scenario). Success rate was quantified by the number of correctly classified steps with respect to the total number of steps. The results indicated that the proposed algorithm's success rate was high (99.3%, 100%, and 98.3% for level, ascent, and descent modes in the stairs scenario, respectively; 98.9%, 97.8%, and 100% in the ramp scenario). The proposed algorithm continuously detected each step's gait mode with faster timing and higher accuracy compared to a previous algorithm that used a decision tree based on maximizing the reliability of the mode recognition. PMID:28070188

An Improved Harmonic Current Detection Method Based on Parallel Active Power Filter

NASA Astrophysics Data System (ADS)

Zeng, Zhiwu; Xie, Yunxiang; Wang, Yingpin; Guan, Yuanpeng; Li, Lanfang; Zhang, Xiaoyu

2017-05-01

Harmonic detection technology plays an important role in the applications of active power filter. The accuracy and real-time performance of harmonic detection are the precondition to ensure the compensation performance of Active Power Filter (APF). This paper proposed an improved instantaneous reactive power harmonic current detection algorithm. The algorithm uses an improved ip -iq algorithm which is combined with the moving average value filter. The proposed ip -iq algorithm can remove the αβ and dq coordinate transformation, decreasing the cost of calculation, simplifying the extraction process of fundamental components of load currents, and improving the detection speed. The traditional low-pass filter is replaced by the moving average filter, detecting the harmonic currents more precisely and quickly. Compared with the traditional algorithm, the THD (Total Harmonic Distortion) of the grid currents is reduced from 4.41% to 3.89% for the simulations and from 8.50% to 4.37% for the experiments after the improvement. The results show the proposed algorithm is more accurate and efficient.

Randomized Dynamic Mode Decomposition

NASA Astrophysics Data System (ADS)

Erichson, N. Benjamin; Brunton, Steven L.; Kutz, J. Nathan

2017-11-01

The dynamic mode decomposition (DMD) is an equation-free, data-driven matrix decomposition that is capable of providing accurate reconstructions of spatio-temporal coherent structures arising in dynamical systems. We present randomized algorithms to compute the near-optimal low-rank dynamic mode decomposition for massive datasets. Randomized algorithms are simple, accurate and able to ease the computational challenges arising with `big data'. Moreover, randomized algorithms are amenable to modern parallel and distributed computing. The idea is to derive a smaller matrix from the high-dimensional input data matrix using randomness as a computational strategy. Then, the dynamic modes and eigenvalues are accurately learned from this smaller representation of the data, whereby the approximation quality can be controlled via oversampling and power iterations. Here, we present randomized DMD algorithms that are categorized by how many passes the algorithm takes through the data. Specifically, the single-pass randomized DMD does not require data to be stored for subsequent passes. Thus, it is possible to approximately decompose massive fluid flows (stored out of core memory, or not stored at all) using single-pass algorithms, which is infeasible with traditional DMD algorithms.

Real-time MSE measurements for current profile control on KSTAR.

PubMed

De Bock, M F M; Aussems, D; Huijgen, R; Scheffer, M; Chung, J

2012-10-01

To step up from current day fusion experiments to power producing fusion reactors, it is necessary to control long pulse, burning plasmas. Stability and confinement properties of tokamak fusion reactors are determined by the current or q profile. In order to control the q profile, it is necessary to measure it in real-time. A real-time motional Stark effect diagnostic is being developed at Korean Superconducting Tokamak for Advanced Research for this purpose. This paper focuses on 3 topics important for real-time measurements: minimize the use of ad hoc parameters, minimize external influences and a robust and fast analysis algorithm. Specifically, we have looked into extracting the retardance of the photo-elastic modulators from the signal itself, minimizing the influence of overlapping beam spectra by optimizing the optical filter design and a multi-channel, multiharmonic phase locking algorithm.

Applied Distributed Model Predictive Control for Energy Efficient Buildings and Ramp Metering

NASA Astrophysics Data System (ADS)

Koehler, Sarah Muraoka

Industrial large-scale control problems present an interesting algorithmic design challenge. A number of controllers must cooperate in real-time on a network of embedded hardware with limited computing power in order to maximize system efficiency while respecting constraints and despite communication delays. Model predictive control (MPC) can automatically synthesize a centralized controller which optimizes an objective function subject to a system model, constraints, and predictions of disturbance. Unfortunately, the computations required by model predictive controllers for large-scale systems often limit its industrial implementation only to medium-scale slow processes. Distributed model predictive control (DMPC) enters the picture as a way to decentralize a large-scale model predictive control problem. The main idea of DMPC is to split the computations required by the MPC problem amongst distributed processors that can compute in parallel and communicate iteratively to find a solution. Some popularly proposed solutions are distributed optimization algorithms such as dual decomposition and the alternating direction method of multipliers (ADMM). However, these algorithms ignore two practical challenges: substantial communication delays present in control systems and also problem non-convexity. This thesis presents two novel and practically effective DMPC algorithms. The first DMPC algorithm is based on a primal-dual active-set method which achieves fast convergence, making it suitable for large-scale control applications which have a large communication delay across its communication network. In particular, this algorithm is suited for MPC problems with a quadratic cost, linear dynamics, forecasted demand, and box constraints. We measure the performance of this algorithm and show that it significantly outperforms both dual decomposition and ADMM in the presence of communication delay. The second DMPC algorithm is based on an inexact interior point method which is suited for nonlinear optimization problems. The parallel computation of the algorithm exploits iterative linear algebra methods for the main linear algebra computations in the algorithm. We show that the splitting of the algorithm is flexible and can thus be applied to various distributed platform configurations. The two proposed algorithms are applied to two main energy and transportation control problems. The first application is energy efficient building control. Buildings represent 40% of energy consumption in the United States. Thus, it is significant to improve the energy efficiency of buildings. The goal is to minimize energy consumption subject to the physics of the building (e.g. heat transfer laws), the constraints of the actuators as well as the desired operating constraints (thermal comfort of the occupants), and heat load on the system. In this thesis, we describe the control systems of forced air building systems in practice. We discuss the "Trim and Respond" algorithm which is a distributed control algorithm that is used in practice, and show that it performs similarly to a one-step explicit DMPC algorithm. Then, we apply the novel distributed primal-dual active-set method and provide extensive numerical results for the building MPC problem. The second main application is the control of ramp metering signals to optimize traffic flow through a freeway system. This application is particularly important since urban congestion has more than doubled in the past few decades. The ramp metering problem is to maximize freeway throughput subject to freeway dynamics (derived from mass conservation), actuation constraints, freeway capacity constraints, and predicted traffic demand. In this thesis, we develop a hybrid model predictive controller for ramp metering that is guaranteed to be persistently feasible and stable. This contrasts to previous work on MPC for ramp metering where such guarantees are absent. We apply a smoothing method to the hybrid model predictive controller and apply the inexact interior point method to this nonlinear non-convex ramp metering problem.

Optimized MPPT-based converter for TEG energy harvester to power wireless sensor and monitoring system in nuclear power plant

NASA Astrophysics Data System (ADS)

Xing, Shaoxu; Anakok, Isil; Zuo, Lei

2017-04-01

Accidents like Fukushima Disasters push people to improve the monitoring systems for the nuclear power plants. Thus, various types of energy harvesters are designed to power these systems and the Thermoelectric Generator (TEG) energy harvester is one of them. In order to enhance the amount of harvested power and the system efficiency, the power management stage needs to be carefully designed. In this paper, a power converter with optimized Maximum Power Point Tracking (MPPT) is proposed for the TEG Energy Harvester to power the wireless sensor network in nuclear power plant. The TEG Energy Harvester is installed on the coolant pipe of the nuclear plant and harvests energy from its heat energy while the power converter with optimized MPPT can make the TEG Energy Harvester output the maximum power, quickly response to the voltage change and provide sufficient energy for wireless sensor system to monitor the operation of the nuclear power plant. Due to the special characteristics of the Single-Ended Primary Inductor Converter (SEPIC) when it is working in the Discontinuous Inductor Current Mode (DICM) and Continuous Conduction Mode (CCM), the MPPT method presented in this paper would be able to control the converter to achieve the maximum output power in any working conditions of the TEG system with a simple circuit. The optimized MPPT algorithm will significantly reduce the cost and simplify the system as well as achieve a good performance. Experiment test results have shown that, comparing to a fixed- duty-cycle SEPIC which is specifically designed for the working on the secondary coolant loop in nuclear power plant, the optimized MPPT algorithm increased the output power by 55%.

Method for Prediction of the Power Output from Photovoltaic Power Plant under Actual Operating Conditions

NASA Astrophysics Data System (ADS)

Obukhov, S. G.; Plotnikov, I. A.; Surzhikova, O. A.; Savkin, K. D.

2017-04-01

Solar photovoltaic technology is one of the most rapidly growing renewable sources of electricity that has practical application in various fields of human activity due to its high availability, huge potential and environmental compatibility. The original simulation model of the photovoltaic power plant has been developed to simulate and investigate the plant operating modes under actual operating conditions. The proposed model considers the impact of the external climatic factors on the solar panel energy characteristics that improves accuracy in the power output prediction. The data obtained through the photovoltaic power plant operation simulation enable a well-reasoned choice of the required capacity for storage devices and determination of the rational algorithms to control the energy complex.

Study of efficient video compression algorithms for space shuttle applications

NASA Technical Reports Server (NTRS)

Poo, Z.

1975-01-01

Results are presented of a study on video data compression techniques applicable to space flight communication. This study is directed towards monochrome (black and white) picture communication with special emphasis on feasibility of hardware implementation. The primary factors for such a communication system in space flight application are: picture quality, system reliability, power comsumption, and hardware weight. In terms of hardware implementation, these are directly related to hardware complexity, effectiveness of the hardware algorithm, immunity of the source code to channel noise, and data transmission rate (or transmission bandwidth). A system is recommended, and its hardware requirement summarized. Simulations of the study were performed on the improved LIM video controller which is computer-controlled by the META-4 CPU.

Neurofeedback Training for BCI Control

NASA Astrophysics Data System (ADS)

Neuper, Christa; Pfurtscheller, Gert

Brain-computer interface (BCI) systems detect changes in brain signals that reflect human intention, then translate these signals to control monitors or external devices (for a comprehensive review, see [1]). BCIs typically measure electrical signals resulting from neural firing (i.e. neuronal action potentials, Electroencephalogram (ECoG), or Electroencephalogram (EEG)). Sophisticated pattern recognition and classification algorithms convert neural activity into the required control signals. BCI research has focused heavily on developing powerful signal processing and machine learning techniques to accurately classify neural activity [2-4].

Identification of two-phase flow regime based on electrical capacitance tomography and soft-sensing technique

NASA Astrophysics Data System (ADS)

Zhao, Ming-fu; Hu, Xin-Yu; Shao, Yun; Luo, Bin-bin; Wang, Xin

2008-10-01

This article analyses nowadays in common use of football robots in China, intended to improve the football robots' hardware platform system's capability, and designed a football robot which based on DSP core controller, and combined Fuzzy-PID control algorithm. The experiment showed, because of the advantages of DSP, such as quickly operation, various of interfaces, low power dissipation etc. It has great improvement on the football robot's performance of movement, controlling precision, real-time performance.

The Use of a Gyroless Wheel-Tach Controller in SDO Safehold Mode

NASA Technical Reports Server (NTRS)

Bourkland, Kristin L.; Starin, Scott R.; Mangus, David J.; Starin, Scott (Technical Monitor)

2005-01-01

This paper describes the progression of the Safehold mode design on the Solar Dynamics Observatory satellite. Safehold uses coarse Sun sensors and reaction wheel tachometers to keep the spacecraft in a thermally safe and power-positive attitude. The control algorithm is described, and simulation results shown. Specific control issues arose when the spacecraft entered eclipse, and a description of the trade study which added gyroscopes to the mode is included. The paper concludes with the results from the linear and nonlinear stability analysis.

Voltage oriented control of self-excited induction generator for wind energy system with MPPT

NASA Astrophysics Data System (ADS)

Amieur, Toufik; Taibi, Djamel; Amieur, Oualid

2018-05-01

This paper presents the study and simulation of the self-excited induction generator in the wind power production in isolated sites. With this intention, a model of the wind turbine was established. Extremum-seeking control algorithm method by using Maximum Power Point Tracking (MPPT) is proposed control solution aims at driving the average position of the operating point near to optimality. The reference of turbine rotor speed is adjusted such that the turbine operates around maximum power for the current wind speed value. After a brief review of the concepts of converting wind energy into electrical energy. The proposed modeling tools were developed to study the performance of standalone induction generators connected to capacitor bank. The purpose of this technique is to maintain a constant voltage at the output of the rectifier whatever the loads and speeds. The system studied in this work is developed and tested in MATLAB/Simulink environment. Simulation results validate the performance and effectiveness of the proposed control methods.

A control strategy for PV stand-alone applications

NASA Astrophysics Data System (ADS)

Slouma, S.; Baccar, H.

2015-04-01

This paper proposes a stand-alone photovoltaic (PV) system study in domestic applications. Because of the decrease in power of photovoltaic module as a consequence of changes in solar radiation and temperature which affect the photovoltaic module performance, the design and control of DC-DC buck converter was proposed for providing power to the load from a photovoltaic source.In fact, the control of this converter is carried out with integrated MPPT (Maximum Power Point Tracking) algorithm which ensures a maximum energy generated by the PV arrays. Moreover, the output stage is composed by a battery energy storage system, dc-ac inverter, LCL filter which enables higher efficiency, low distortion ac waveforms and low leakage currents. The control strategy adopted is cascade control composed by two regulation loops.Simulations performed with PSIM software were able to validate the control system.The realization and testing of the photovoltaic system were achieved in the Photovoltaic laboratory of the Centre for Research and Energy Technologies at the Technopark Borj Cedria. Experimental results verify the effeciency of the proposed system.

Combinational Circuit Obfuscation Through Power Signature Manipulation

DTIC Science & Technology

2011-06-01

Algorithm produced by SID . . . . . . . . . . . . . . . . . . . . . . 80 Appendix B . Power Signature Estimation Results 2 . . . . . . . . . . 85 B .1 Power...Signature for c264 Circuit Variant per Algorithm produced by SPICE Simulation . . . . . . . . . . . . . . 85 B .2 Power Signature for c5355 and c499...Smart SSR selecting rear level components and gates with 1000 iterations . . . . . . . . . 84 B .1. Power Signature for c264 By Random Sequence

Statistics based sampling for controller and estimator design

NASA Astrophysics Data System (ADS)

Tenne, Dirk

The purpose of this research is the development of statistical design tools for robust feed-forward/feedback controllers and nonlinear estimators. This dissertation is threefold and addresses the aforementioned topics nonlinear estimation, target tracking and robust control. To develop statistically robust controllers and nonlinear estimation algorithms, research has been performed to extend existing techniques, which propagate the statistics of the state, to achieve higher order accuracy. The so-called unscented transformation has been extended to capture higher order moments. Furthermore, higher order moment update algorithms based on a truncated power series have been developed. The proposed techniques are tested on various benchmark examples. Furthermore, the unscented transformation has been utilized to develop a three dimensional geometrically constrained target tracker. The proposed planar circular prediction algorithm has been developed in a local coordinate framework, which is amenable to extension of the tracking algorithm to three dimensional space. This tracker combines the predictions of a circular prediction algorithm and a constant velocity filter by utilizing the Covariance Intersection. This combined prediction can be updated with the subsequent measurement using a linear estimator. The proposed technique is illustrated on a 3D benchmark trajectory, which includes coordinated turns and straight line maneuvers. The third part of this dissertation addresses the design of controller which include knowledge of parametric uncertainties and their distributions. The parameter distributions are approximated by a finite set of points which are calculated by the unscented transformation. This set of points is used to design robust controllers which minimize a statistical performance of the plant over the domain of uncertainty consisting of a combination of the mean and variance. The proposed technique is illustrated on three benchmark problems. The first relates to the design of prefilters for a linear and nonlinear spring-mass-dashpot system and the second applies a feedback controller to a hovering helicopter. Lastly, the statistical robust controller design is devoted to a concurrent feed-forward/feedback controller structure for a high-speed low tension tape drive.

An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease

PubMed Central

Schmitter, Daniel; Roche, Alexis; Maréchal, Bénédicte; Ribes, Delphine; Abdulkadir, Ahmed; Bach-Cuadra, Meritxell; Daducci, Alessandro; Granziera, Cristina; Klöppel, Stefan; Maeder, Philippe; Meuli, Reto; Krueger, Gunnar

2014-01-01

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease. PMID:25429357

Design of bearings for rotor systems based on stability

NASA Technical Reports Server (NTRS)

Dhar, D.; Barrett, L. E.; Knospe, C. R.

1992-01-01

Design of rotor systems incorporating stable behavior is of great importance to manufacturers of high speed centrifugal machinery since destabilizing mechanisms (from bearings, seals, aerodynamic cross coupling, noncolocation effects from magnetic bearings, etc.) increase with machine efficiency and power density. A new method of designing bearing parameters (stiffness and damping coefficients or coefficients of the controller transfer function) is proposed, based on a numerical search in the parameter space. The feedback control law is based on a decentralized low order controller structure, and the various design requirements are specified as constraints in the specification and parameter spaces. An algorithm is proposed for solving the problem as a sequence of constrained 'minimax' problems, with more and more eigenvalues into an acceptable region in the complex plane. The algorithm uses the method of feasible directions to solve the nonlinear constrained minimization problem at each stage. This methodology emphasizes the designer's interaction with the algorithm to generate acceptable designs by relaxing various constraints and changing initial guesses interactively. A design oriented user interface is proposed to facilitate the interaction.

Powered ankle-foot prosthesis to assist level-ground and stair-descent gaits.

PubMed

Au, Samuel; Berniker, Max; Herr, Hugh

2008-05-01

The human ankle varies impedance and delivers net positive work during the stance period of walking. In contrast, commercially available ankle-foot prostheses are passive during stance, causing many clinical problems for transtibial amputees, including non-symmetric gait patterns, higher gait metabolism, and poorer shock absorption. In this investigation, we develop and evaluate a myoelectric-driven, finite state controller for a powered ankle-foot prosthesis that modulates both impedance and power output during stance. The system employs both sensory inputs measured local to the external prosthesis, and myoelectric inputs measured from residual limb muscles. Using local prosthetic sensing, we first develop two finite state controllers to produce biomimetic movement patterns for level-ground and stair-descent gaits. We then employ myoelectric signals as control commands to manage the transition between these finite state controllers. To transition from level-ground to stairs, the amputee flexes the gastrocnemius muscle, triggering the prosthetic ankle to plantar flex at terminal swing, and initiating the stair-descent state machine algorithm. To transition back to level-ground walking, the amputee flexes the tibialis anterior muscle, triggering the ankle to remain dorsiflexed at terminal swing, and initiating the level-ground state machine algorithm. As a preliminary evaluation of clinical efficacy, we test the device on a transtibial amputee with both the proposed controller and a conventional passive-elastic control. We find that the amputee can robustly transition between the finite state controllers through direct muscle activation, allowing rapid transitioning from level-ground to stair walking patterns. Additionally, we find that the proposed finite state controllers result in a more biomimetic ankle response, producing net propulsive work during level-ground walking and greater shock absorption during stair descent. The results of this study highlight the potential of prosthetic leg controllers that exploit neural signals to trigger terrain-appropriate, local prosthetic leg behaviors.

A Three-Stage Enhanced Reactive Power and Voltage Optimization Method for High Penetration of Solar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ke, Xinda; Huang, Renke; Vallem, Mallikarjuna R.

This paper presents a three-stage enhanced volt/var optimization method to stabilize voltage fluctuations in transmission networks by optimizing the usage of reactive power control devices. In contrast with existing volt/var optimization algorithms, the proposed method optimizes the voltage profiles of the system, while keeping the voltage and real power output of the generators as close to the original scheduling values as possible. This allows the method to accommodate realistic power system operation and market scenarios, in which the original generation dispatch schedule will not be affected. The proposed method was tested and validated on a modified IEEE 118-bus system withmore » photovoltaic data.« less

System-level power optimization for real-time distributed embedded systems

NASA Astrophysics Data System (ADS)

Luo, Jiong

Power optimization is one of the crucial design considerations for modern electronic systems. In this thesis, we present several system-level power optimization techniques for real-time distributed embedded systems, based on dynamic voltage scaling, dynamic power management, and management of peak power and variance of the power profile. Dynamic voltage scaling has been widely acknowledged as an important and powerful technique to trade off dynamic power consumption and delay. Efficient dynamic voltage scaling requires effective variable-voltage scheduling mechanisms that can adjust voltages and clock frequencies adaptively based on workloads and timing constraints. For this purpose, we propose static variable-voltage scheduling algorithms utilizing criticalpath driven timing analysis for the case when tasks are assumed to have uniform switching activities, as well as energy-gradient driven slack allocation for a more general scenario. The proposed techniques can achieve closeto-optimal power savings with very low computational complexity, without violating any real-time constraints. We also present algorithms for power-efficient joint scheduling of multi-rate periodic task graphs along with soft aperiodic tasks. The power issue is addressed through both dynamic voltage scaling and power management. Periodic task graphs are scheduled statically. Flexibility is introduced into the static schedule to allow the on-line scheduler to make local changes to PE schedules through resource reclaiming and slack stealing, without interfering with the validity of the global schedule. We provide a unified framework in which the response times of aperiodic tasks and power consumption are dynamically optimized simultaneously. Interconnection network fabrics point to a new generation of power-efficient and scalable interconnection architectures for distributed embedded systems. As the system bandwidth continues to increase, interconnection networks become power/energy limited as well. Variable-frequency links have been designed by circuit designers for both parallel and serial links, which can adaptively regulate the supply voltage of transceivers to a desired link frequency, to exploit the variations in bandwidth requirement for power savings. We propose solutions for simultaneous dynamic voltage scaling of processors and links. The proposed solution considers real-time scheduling, flow control, and packet routing jointly. It can trade off the power consumption on processors and communication links via efficient slack allocation, and lead to more power savings than dynamic voltage scaling on processors alone. For battery-operated systems, the battery lifespan is an important concern. Due to the effects of discharge rate and battery recovery, the discharge pattern of batteries has an impact on the battery lifespan. Battery models indicate that even under the same average power consumption, reducing peak power current and variance in the power profile can increase the battery efficiency and thereby prolong battery lifetime. To take advantage of these effects, we propose battery-driven scheduling techniques for embedded applications, to reduce the peak power and the variance in the power profile of the overall system under real-time constraints. The proposed scheduling algorithms are also beneficial in addressing reliability and signal integrity concerns by effectively controlling peak power and variance of the power profile.

Minimum-Time Consensus-Based Approach for Power System Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Tao; Wu, Di; Sun, Yannan

2016-02-01

This paper presents minimum-time consensus based distributed algorithms for power system applications, such as load shedding and economic dispatch. The proposed algorithms are capable of solving these problems in a minimum number of time steps instead of asymptotically as in most of existing studies. Moreover, these algorithms are applicable to both undirected and directed communication networks. Simulation results are used to validate the proposed algorithms.

Study on additional carrier sensing for IEEE 802.15.4 wireless sensor networks.

PubMed

Lee, Bih-Hwang; Lai, Ruei-Lung; Wu, Huai-Kuei; Wong, Chi-Ming

2010-01-01

Wireless sensor networks based on the IEEE 802.15.4 standard are able to achieve low-power transmissions in the guise of low-rate and short-distance wireless personal area networks (WPANs). The slotted carrier sense multiple access with collision avoidance (CSMA/CA) is used for contention mechanism. Sensor nodes perform a backoff process as soon as the clear channel assessment (CCA) detects a busy channel. In doing so they may neglect the implicit information of the failed CCA detection and further cause the redundant sensing. The blind backoff process in the slotted CSMA/CA will cause lower channel utilization. This paper proposes an additional carrier sensing (ACS) algorithm based on IEEE 802.15.4 to enhance the carrier sensing mechanism for the original slotted CSMA/CA. An analytical Markov chain model is developed to evaluate the performance of the ACS algorithm. Both analytical and simulation results show that the proposed algorithm performs better than IEEE 802.15.4, which in turn significantly improves throughput, average medium access control (MAC) delay and power consumption of CCA detection.

A 12b 200kS/s 0.52mA 0.47mm2 Algorithmic A/D Converter for MEMS Applications

NASA Astrophysics Data System (ADS)

Kim, Young-Ju; Choi, Hee-Cheol; Lee, Seung-Hoon; Cho, Dongil “Dan”

This work describes a 12b 200kS/s 0.52mA 0.47mm2 ADC for sensor applications such as motor control, 3-phase power control, and CMOS image sensors simultaneously requiring ultra-low power and small size. The proposed ADC is based on the conventional algorithmic architecture with a recycling signal path to optimize sampling rate, resolution, chip area, and power consumption. The input SHA with eight input channels employs a folded-cascode amplifier to achieve a required DC gain and a high phase margin. A 3-D fully symmetric layout with critical signal lines shielded reduces the capacitor and device mismatch of the multiplying D/A converter while switched-bias power-reduction circuits minimize the power consumption of analog amplifiers. Current and voltage references are integrated on chip with optional off-chip voltage references for low glitch noise. The down-sampling clock signal selects the sampling rate of 200kS/s and 10kS/s with a further reduced power depending on applications. The prototype ADC in a 0.18μm n-well 1P6M CMOS process demonstrates a maximum measured DNL and INL within 0.40 LSB and 1.97 LSB and shows a maximum SNDR and SFDR of 55dB and 70dB at all sampling frequencies up to 200kS/s, respectively. The ADC occupies an active die area of 0.47mm2 and consumes 0.94mW at 200kS/s and 0.63mW at 10kS/s with a 1.8V supply.

Quantum Computation: Entangling with the Future

NASA Technical Reports Server (NTRS)

Jiang, Zhang

2017-01-01

Commercial applications of quantum computation have become viable due to the rapid progress of the field in the recent years. Efficient quantum algorithms are discovered to cope with the most challenging real-world problems that are too hard for classical computers. Manufactured quantum hardware has reached unprecedented precision and controllability, enabling fault-tolerant quantum computation. Here, I give a brief introduction on what principles in quantum mechanics promise its unparalleled computational power. I will discuss several important quantum algorithms that achieve exponential or polynomial speedup over any classical algorithm. Building a quantum computer is a daunting task, and I will talk about the criteria and various implementations of quantum computers. I conclude the talk with near-future commercial applications of a quantum computer.

XML in an Adaptive Framework for Instrument Control

NASA Technical Reports Server (NTRS)

Ames, Troy J.

2004-01-01

NASA Goddard Space Flight Center is developing an extensible framework for instrument command and control, known as Instrument Remote Control (IRC), that combines the platform independent processing capabilities of Java with the power of the Extensible Markup Language (XML). A key aspect of the architecture is software that is driven by an instrument description, written using the Instrument Markup Language (IML). IML is an XML dialect used to describe interfaces to control and monitor the instrument, command sets and command formats, data streams, communication mechanisms, and data processing algorithms.

Electric machine differential for vehicle traction control and stability control

NASA Astrophysics Data System (ADS)

Kuruppu, Sandun Shivantha

Evolving requirements in energy efficiency and tightening regulations for reliable electric drivetrains drive the advancement of the hybrid electric (HEV) and full electric vehicle (EV) technology. Different configurations of EV and HEV architectures are evaluated for their performance. The future technology is trending towards utilizing distinctive properties in electric machines to not only to improve efficiency but also to realize advanced road adhesion controls and vehicle stability controls. Electric machine differential (EMD) is such a concept under current investigation for applications in the near future. Reliability of a power train is critical. Therefore, sophisticated fault detection schemes are essential in guaranteeing reliable operation of a complex system such as an EMD. The research presented here emphasize on implementation of a 4kW electric machine differential, a novel single open phase fault diagnostic scheme, an implementation of a real time slip optimization algorithm and an electric machine differential based yaw stability improvement study. The proposed d-q current signature based SPO fault diagnostic algorithm detects the fault within one electrical cycle. The EMD based extremum seeking slip optimization algorithm reduces stopping distance by 30% compared to hydraulic braking based ABS.

Optimal placement of FACTS devices using optimization techniques: A review

NASA Astrophysics Data System (ADS)

Gaur, Dipesh; Mathew, Lini

2018-03-01

Modern power system is dealt with overloading problem especially transmission network which works on their maximum limit. Today’s power system network tends to become unstable and prone to collapse due to disturbances. Flexible AC Transmission system (FACTS) provides solution to problems like line overloading, voltage stability, losses, power flow etc. FACTS can play important role in improving static and dynamic performance of power system. FACTS devices need high initial investment. Therefore, FACTS location, type and their rating are vital and should be optimized to place in the network for maximum benefit. In this paper, different optimization methods like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) etc. are discussed and compared for optimal location, type and rating of devices. FACTS devices such as Thyristor Controlled Series Compensator (TCSC), Static Var Compensator (SVC) and Static Synchronous Compensator (STATCOM) are considered here. Mentioned FACTS controllers effects on different IEEE bus network parameters like generation cost, active power loss, voltage stability etc. have been analyzed and compared among the devices.

Lunar PMAD technology assessment

NASA Technical Reports Server (NTRS)

Metcalf, Kenneth J.

1992-01-01

This report documents an initial set of power conditioning models created to generate 'ballpark' power management and distribution (PMAD) component mass and size estimates. It contains converter, rectifier, inverter, transformer, remote bus isolator (RBI), and remote power controller (RPC) models. These models allow certain studies to be performed; however, additional models are required to assess a full range of PMAD alternatives. The intent is to eventually form a library of PMAD models that will allow system designers to evaluate various power system architectures and distribution techniques quickly and consistently. The models in this report are designed primarily for space exploration initiative (SEI) missions requiring continuous power and supporting manned operations. The mass estimates were developed by identifying the stages in a component and obtaining mass breakdowns for these stages from near term electronic hardware elements. Technology advances were then incorporated to generate hardware masses consistent with the 2000 to 2010 time period. The mass of a complete component is computed by algorithms that calculate the masses of the component stages, control and monitoring, enclosure, and thermal management subsystem.

Prediction and Control of Network Cascade: Example of Power Grid or Networking Adaptability from WMD Disruption and Cascading Failures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chertkov, Michael

2012-07-24

The goal of the DTRA project is to develop a mathematical framework that will provide the fundamental understanding of network survivability, algorithms for detecting/inferring pre-cursors of abnormal network behaviors, and methods for network adaptability and self-healing from cascading failures.

Advanced Energy Storage Management in Distribution Network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Guodong; Ceylan, Oguzhan; Xiao, Bailu

2016-01-01

With increasing penetration of distributed generation (DG) in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. In this paper, an iterative mixed integer quadratic constrained quadratic programming model to optimize the operation of a three phase unbalanced distribution system with high penetration of Photovoltaic (PV) panels, DG and energy storage (ES) is developed. The proposed model minimizes not only the operating cost, including fuel cost and purchasing cost, but also voltage deviations and power loss. The optimization model is based on the linearized sensitivity coefficients between state variables (e.g., node voltages) andmore » control variables (e.g., real and reactive power injections of DG and ES). To avoid slow convergence when close to the optimum, a golden search method is introduced to control the step size and accelerate the convergence. The proposed algorithm is demonstrated on modified IEEE 13 nodes test feeders with multiple PV panels, DG and ES. Numerical simulation results validate the proposed algorithm. Various scenarios of system configuration are studied and some critical findings are concluded.« less

Electric Power Engineering Cost Predicting Model Based on the PCA-GA-BP

NASA Astrophysics Data System (ADS)

Wen, Lei; Yu, Jiake; Zhao, Xin

2017-10-01

In this paper a hybrid prediction algorithm: PCA-GA-BP model is proposed. PCA algorithm is established to reduce the correlation between indicators of original data and decrease difficulty of BP neural network in complex dimensional calculation. The BP neural network is established to estimate the cost of power transmission project. The results show that PCA-GA-BP algorithm can improve result of prediction of electric power engineering cost.

Design of Intelligent Hydraulic Excavator Control System Based on PID Method

NASA Astrophysics Data System (ADS)

Zhang, Jun; Jiao, Shengjie; Liao, Xiaoming; Yin, Penglong; Wang, Yulin; Si, Kuimao; Zhang, Yi; Gu, Hairong

Most of the domestic designed hydraulic excavators adopt the constant power design method and set 85%~90% of engine power as the hydraulic system adoption power, it causes high energy loss due to mismatching of power between the engine and the pump. While the variation of the rotational speed of engine could sense the power shift of the load, it provides a new method to adjust the power matching between engine and pump through engine speed. Based on negative flux hydraulic system, an intelligent hydraulic excavator control system was designed based on rotational speed sensing method to improve energy efficiency. The control system was consisted of engine control module, pump power adjusted module, engine idle module and system fault diagnosis module. Special PLC with CAN bus was used to acquired the sensors and adjusts the pump absorption power according to load variation. Four energy saving control strategies with constant power method were employed to improve the fuel utilization. Three power modes (H, S and L mode) were designed to meet different working status; Auto idle function was employed to save energy through two work status detected pressure switches, 1300rpm was setting as the idle speed according to the engine consumption fuel curve. Transient overload function was designed for deep digging within short time without spending extra fuel. An increasing PID method was employed to realize power matching between engine and pump, the rotational speed's variation was taken as the PID algorithm's input; the current of proportional valve of variable displacement pump was the PID's output. The result indicated that the auto idle could decrease fuel consumption by 33.33% compared to work in maximum speed of H mode, the PID control method could take full use of maximum engine power at each power mode and keep the engine speed at stable range. Application of rotational speed sensing method provides a reliable method to improve the excavator's energy efficiency and realize power match between pump and engine.

Models and methods for assessing the value of HVDC and MVDC technologies in modern power grids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarov, Yuri V.; Elizondo, Marcelo A.; O'Brien, James G.

This report reflects the results of U.S. Department of Energy’s (DOE) Grid Modernization project 0074 “Models and methods for assessing the value of HVDC [high-voltage direct current] and MTDC [multi-terminal direct current] technologies in modern power grids.” The work was done by the Pacific Northwest National Laboratory (PNNL) and Oak Ridge National Laboratory (ORNL) in cooperation with Mid-Continent Independent System Operator (MISO) and Siemens. The main motivation of this study was to show the benefit of using direct current (DC) systems larger than those in existence today as they overlap with the alternating current (AC) systems. Proper use of theirmore » flexibility in terms of active/reactive power control and fast response can provide much-needed services to the grid at the same time as moving large blocks of energy to take advantage of cost diversity. Ultimately, the project’s success will enable decision-makers and investors to make well-informed decisions regarding this use of DC systems. This project showed the technical feasibility of HVDC macrogrid for frequency control and congestion relief in addition to bulk power transfers. Industry-established models for commonly used technologies were employed, along with high-fidelity models for recently developed HVDC converter technologies; like the modular multilevel converters (MMCs), a voltage source converters (VSC). Models for General Electric Positive Sequence Load Flow (GE PSLF) and Siemens Power System Simulator (PSS/E), widely used analysis programs, were for the first time adapted to include at the same time both Western Electricity Coordinating Council (WECC) and Eastern Interconnection (EI), the two largest North American interconnections. The high-fidelity models and their control were developed in detail for MMC system and extended to HVDC systems in point-to-point and in three-node multi-terminal configurations. Using a continental-level mixed AC-DC grid model, and using a HVDC macrogrid power flow and transient stability model, the results showed that the HVDC macrogrid relieved congestion and mitigated loop flows in AC networks, and provided up to 24% improvement in frequency responses. These are realistic studies, based on the 2025 heavy summer and EI multi-regional modeling working group (MMWG) 2026 summer peak cases. This work developed high-fidelity models and simulation algorithms to understand the dynamics of MMC. The developed models and simulation algorithms are up to 25 times faster than the existing algorithms. Models and control algorithms for high-fidelity models were designed and tested for point-to-point and multi-terminal configurations. The multi-terminal configuration was tested connecting simplified models of EI, WI, and Electric Reliability Council of Texas (ERCOT). The developed models showed up to 45% improvement in frequency response with the connection of all the three asynchronous interconnections in the United States using fast and advanced DC technologies like the multi-terminal MMC-DC system. Future work will look into developing high-fidelity models of other advanced DC technologies, combining high-fidelity models with the continental-level model, incorporating additional services. More scenarios involving large-scale HVDC and MTDC will be evaluated.« less

Smart surgical tool

NASA Astrophysics Data System (ADS)

Huang, Huan; Yang, Lih-Mei; Bai, Shuang; Liu, Jian

2015-02-01

A laser-induced breakdown spectroscopy (LIBS) guided smart surgical tool using a femtosecond fiber laser is developed. This system provides real-time material identification by processing and analyzing the peak intensity and ratio of atomic emissions of LIBS signals. Algorithms to identify emissions of different tissues and metals are developed and implemented into the real-time control system. This system provides a powerful smart surgical tool for precise robotic microsurgery applications with real-time feedback and control.

A novel variational Bayes multiple locus Z-statistic for genome-wide association studies with Bayesian model averaging

PubMed Central

Logsdon, Benjamin A.; Carty, Cara L.; Reiner, Alexander P.; Dai, James Y.; Kooperberg, Charles

2012-01-01

Motivation: For many complex traits, including height, the majority of variants identified by genome-wide association studies (GWAS) have small effects, leaving a significant proportion of the heritable variation unexplained. Although many penalized multiple regression methodologies have been proposed to increase the power to detect associations for complex genetic architectures, they generally lack mechanisms for false-positive control and diagnostics for model over-fitting. Our methodology is the first penalized multiple regression approach that explicitly controls Type I error rates and provide model over-fitting diagnostics through a novel normally distributed statistic defined for every marker within the GWAS, based on results from a variational Bayes spike regression algorithm. Results: We compare the performance of our method to the lasso and single marker analysis on simulated data and demonstrate that our approach has superior performance in terms of power and Type I error control. In addition, using the Women's Health Initiative (WHI) SNP Health Association Resource (SHARe) GWAS of African-Americans, we show that our method has power to detect additional novel associations with body height. These findings replicate by reaching a stringent cutoff of marginal association in a larger cohort. Availability: An R-package, including an implementation of our variational Bayes spike regression (vBsr) algorithm, is available at http://kooperberg.fhcrc.org/soft.html. Contact: blogsdon@fhcrc.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22563072

Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT

PubMed Central

2017-01-01

Cat Swarm Optimization (CSO) algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO) algorithm, the application of CSO is greatly limited by the drawback of “premature convergence,” that is, the possibility of trapping in local optimum when dealing with nonlinear optimization problem with a large number of local extreme values. In order to surmount the shortcomings of CSO, Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed in this paper. Firstly, Quantum-behaved Cat Swarm Optimization (QCSO) algorithm improves the accuracy of the CSO algorithm, because it is easy to fall into the local optimum in the later stage. Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed by introducing tent map for jumping out of local optimum in this paper. Secondly, CQCSO has been applied in the simulation of five different test functions, showing higher accuracy and less time consumption than CSO and QCSO. Finally, photovoltaic MPPT model and experimental platform are established and global maximum power point tracking control strategy is achieved by CQCSO algorithm, the effectiveness and efficiency of which have been verified by both simulation and experiment. PMID:29181020

Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT.

PubMed

Nie, Xiaohua; Wang, Wei; Nie, Haoyao

2017-01-01

Cat Swarm Optimization (CSO) algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO) algorithm, the application of CSO is greatly limited by the drawback of "premature convergence," that is, the possibility of trapping in local optimum when dealing with nonlinear optimization problem with a large number of local extreme values. In order to surmount the shortcomings of CSO, Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed in this paper. Firstly, Quantum-behaved Cat Swarm Optimization (QCSO) algorithm improves the accuracy of the CSO algorithm, because it is easy to fall into the local optimum in the later stage. Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed by introducing tent map for jumping out of local optimum in this paper. Secondly, CQCSO has been applied in the simulation of five different test functions, showing higher accuracy and less time consumption than CSO and QCSO. Finally, photovoltaic MPPT model and experimental platform are established and global maximum power point tracking control strategy is achieved by CQCSO algorithm, the effectiveness and efficiency of which have been verified by both simulation and experiment.

Linear Time Algorithms to Restrict Insider Access using Multi-Policy Access Control Systems

PubMed Central

Mell, Peter; Shook, James; Harang, Richard; Gavrila, Serban

2017-01-01

An important way to limit malicious insiders from distributing sensitive information is to as tightly as possible limit their access to information. This has always been the goal of access control mechanisms, but individual approaches have been shown to be inadequate. Ensemble approaches of multiple methods instantiated simultaneously have been shown to more tightly restrict access, but approaches to do so have had limited scalability (resulting in exponential calculations in some cases). In this work, we take the Next Generation Access Control (NGAC) approach standardized by the American National Standards Institute (ANSI) and demonstrate its scalability. The existing publicly available reference implementations all use cubic algorithms and thus NGAC was widely viewed as not scalable. The primary NGAC reference implementation took, for example, several minutes to simply display the set of files accessible to a user on a moderately sized system. In our approach, we take these cubic algorithms and make them linear. We do this by reformulating the set theoretic approach of the NGAC standard into a graph theoretic approach and then apply standard graph algorithms. We thus can answer important access control decision questions (e.g., which files are available to a user and which users can access a file) using linear time graph algorithms. We also provide a default linear time mechanism to visualize and review user access rights for an ensemble of access control mechanisms. Our visualization appears to be a simple file directory hierarchy but in reality is an automatically generated structure abstracted from the underlying access control graph that works with any set of simultaneously instantiated access control policies. It also provide an implicit mechanism for symbolic linking that provides a powerful access capability. Our work thus provides the first efficient implementation of NGAC while enabling user privilege review through a novel visualization approach. This may help transition from concept to reality the idea of using ensembles of simultaneously instantiated access control methodologies, thereby limiting insider threat. PMID:28758045

A Novel Sensor Selection and Power Allocation Algorithm for Multiple-Target Tracking in an LPI Radar Network

PubMed Central

She, Ji; Wang, Fei; Zhou, Jianjiang

2016-01-01

Radar networks are proven to have numerous advantages over traditional monostatic and bistatic radar. With recent developments, radar networks have become an attractive platform due to their low probability of intercept (LPI) performance for target tracking. In this paper, a joint sensor selection and power allocation algorithm for multiple-target tracking in a radar network based on LPI is proposed. It is found that this algorithm can minimize the total transmitted power of a radar network on the basis of a predetermined mutual information (MI) threshold between the target impulse response and the reflected signal. The MI is required by the radar network system to estimate target parameters, and it can be calculated predictively with the estimation of target state. The optimization problem of sensor selection and power allocation, which contains two variables, is non-convex and it can be solved by separating power allocation problem from sensor selection problem. To be specific, the optimization problem of power allocation can be solved by using the bisection method for each sensor selection scheme. Also, the optimization problem of sensor selection can be solved by a lower complexity algorithm based on the allocated powers. According to the simulation results, it can be found that the proposed algorithm can effectively reduce the total transmitted power of a radar network, which can be conducive to improving LPI performance. PMID:28009819

Computing the Feasible Spaces of Optimal Power Flow Problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Molzahn, Daniel K.

The solution to an optimal power flow (OPF) problem provides a minimum cost operating point for an electric power system. The performance of OPF solution techniques strongly depends on the problem’s feasible space. This paper presents an algorithm that is guaranteed to compute the entire feasible spaces of small OPF problems to within a specified discretization tolerance. Specifically, the feasible space is computed by discretizing certain of the OPF problem’s inequality constraints to obtain a set of power flow equations. All solutions to the power flow equations at each discretization point are obtained using the Numerical Polynomial Homotopy Continuation (NPHC)more » algorithm. To improve computational tractability, “bound tightening” and “grid pruning” algorithms use convex relaxations to preclude consideration of many discretization points that are infeasible for the OPF problem. Here, the proposed algorithm is used to generate the feasible spaces of two small test cases.« less

Computing the Feasible Spaces of Optimal Power Flow Problems

DOE PAGES

Molzahn, Daniel K.

2017-03-15

The solution to an optimal power flow (OPF) problem provides a minimum cost operating point for an electric power system. The performance of OPF solution techniques strongly depends on the problem’s feasible space. This paper presents an algorithm that is guaranteed to compute the entire feasible spaces of small OPF problems to within a specified discretization tolerance. Specifically, the feasible space is computed by discretizing certain of the OPF problem’s inequality constraints to obtain a set of power flow equations. All solutions to the power flow equations at each discretization point are obtained using the Numerical Polynomial Homotopy Continuation (NPHC)more » algorithm. To improve computational tractability, “bound tightening” and “grid pruning” algorithms use convex relaxations to preclude consideration of many discretization points that are infeasible for the OPF problem. Here, the proposed algorithm is used to generate the feasible spaces of two small test cases.« less

Optimization of fuel-cell tram operation based on two dimension dynamic programming

NASA Astrophysics Data System (ADS)

Zhang, Wenbin; Lu, Xuecheng; Zhao, Jingsong; Li, Jianqiu

2018-02-01

This paper proposes an optimal control strategy based on the two-dimension dynamic programming (2DDP) algorithm targeting at minimizing operation energy consumption for a fuel-cell tram. The energy consumption model with the tram dynamics is firstly deduced. Optimal control problem are analyzed and the 2DDP strategy is applied to solve the problem. The optimal tram speed profiles are obtained for each interstation which consist of three stages: accelerate to the set speed with the maximum traction power, dynamically adjust to maintain a uniform speed and decelerate to zero speed with the maximum braking power at a suitable timing. The optimal control curves of all the interstations are connected with the parking time to form the optimal control method of the whole line. The optimized speed profiles are also simplified for drivers to follow.

Distributed synchronization control of complex networks with communication constraints.

PubMed

Xu, Zhenhua; Zhang, Dan; Song, Hongbo

2016-11-01

This paper is concerned with the distributed synchronization control of complex networks with communication constraints. In this work, the controllers communicate with each other through the wireless network, acting as a controller network. Due to the constrained transmission power, techniques such as the packet size reduction and transmission rate reduction schemes are proposed which could help reduce communication load of the controller network. The packet dropout problem is also considered in the controller design since it is often encountered in networked control systems. We show that the closed-loop system can be modeled as a switched system with uncertainties and random variables. By resorting to the switched system approach and some stochastic system analysis method, a new sufficient condition is firstly proposed such that the exponential synchronization is guaranteed in the mean-square sense. The controller gains are determined by using the well-known cone complementarity linearization (CCL) algorithm. Finally, a simulation study is performed, which demonstrates the effectiveness of the proposed design algorithm. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Design and initial validation of a wireless control system based on WSN

NASA Astrophysics Data System (ADS)

Yu, Yan; Li, Luyu; Li, Peng; Wang, Xu; Liu, Hang; Ou, Jinping

2013-04-01

At present, cantilever structure used widely in civil structures will generate continuous vibration by external force due to their low damping characteristic, which leads to a serious impact on the working performance and service time. Therefore, it is very important to control the vibration of these structures. The active vibration control is the primary means of controlling the vibration with high precision and strong adaptive ability. Nowadays, there are many researches using piezoelectric materials in the structural vibration control. Piezoelectric materials are cheap, reliable and they can provide braking and sensing method harmless to the structure, therefore they have broad usage. They are used for structural vibration control in a lot of civil engineering research currently. In traditional sensor applications, information exchanges with the monitoring center or a computer system through wires. If wireless sensor networks(WSN) technology is used, cabling links is not needed, thus the cost of the whole system is greatly reduced. Based on the above advantages, a wireless control system is designed and validated through preliminary tests. The system consists of a cantilever, PVDF as sensor, signal conditioning circuit(SCM), A/D acquisition board, control arithmetic unit, D/A output board, power amplifier, piezoelectric bimorph as actuator. DSP chip is used as the control arithmetic unit and PD control algorithm is embedded in it. PVDF collects the parameters of vibration, sends them to the SCM after A/D conversion. SCM passes the data to the DSP through wireless technology, and DSP calculates and outputs the control values according to the control algorithm. The output signal is amplified by the power amplifier to drive the piezoelectric bimorph for vibration control. The structural vibration duration reduces to 1/4 of the uncontrolled case, which verifies the feasibility of the system.

Intelligent and robust optimization frameworks for smart grids

NASA Astrophysics Data System (ADS)

Dhansri, Naren Reddy

A smart grid implies a cyberspace real-time distributed power control system to optimally deliver electricity based on varying consumer characteristics. Although smart grids solve many of the contemporary problems, they give rise to new control and optimization problems with the growing role of renewable energy sources such as wind or solar energy. Under highly dynamic nature of distributed power generation and the varying consumer demand and cost requirements, the total power output of the grid should be controlled such that the load demand is met by giving a higher priority to renewable energy sources. Hence, the power generated from renewable energy sources should be optimized while minimizing the generation from non renewable energy sources. This research develops a demand-based automatic generation control and optimization framework for real-time smart grid operations by integrating conventional and renewable energy sources under varying consumer demand and cost requirements. Focusing on the renewable energy sources, the intelligent and robust control frameworks optimize the power generation by tracking the consumer demand in a closed-loop control framework, yielding superior economic and ecological benefits and circumvent nonlinear model complexities and handles uncertainties for superior real-time operations. The proposed intelligent system framework optimizes the smart grid power generation for maximum economical and ecological benefits under an uncertain renewable wind energy source. The numerical results demonstrate that the proposed framework is a viable approach to integrate various energy sources for real-time smart grid implementations. The robust optimization framework results demonstrate the effectiveness of the robust controllers under bounded power plant model uncertainties and exogenous wind input excitation while maximizing economical and ecological performance objectives. Therefore, the proposed framework offers a new worst-case deterministic optimization algorithm for smart grid automatic generation control.

Reliable and Affordable Control Systems Active Combustor Pattern Factor Control

NASA Technical Reports Server (NTRS)

McCarty, Bob; Tomondi, Chris; McGinley, Ray

2004-01-01

Active, closed-loop control of combustor pattern factor is a cooperative effort between Honeywell (formerly AlliedSignal) Engines and Systems and the NASA Glenn Research Center to reduce emissions and turbine-stator vane temperature variations, thereby enhancing engine performance and life, and reducing direct operating costs. Total fuel flow supplied to the engine is established by the speed/power control, but the distribution to individual atomizers will be controlled by the Active Combustor Pattern Factor Control (ACPFC). This system consist of three major components: multiple, thin-film sensors located on the turbine-stator vanes; fuel-flow modulators for individual atomizers; and control logic and algorithms within the electronic control.

A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system.

PubMed

Mohamed, Ahmed F; Elarini, Mahdi M; Othman, Ahmed M

2014-05-01

One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA) optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt.

A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system

PubMed Central

Mohamed, Ahmed F.; Elarini, Mahdi M.; Othman, Ahmed M.

2013-01-01

One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA) optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt. PMID:25685507

Oven controlled N++ [1 0 0] length-extensional mode silicon resonator with frequency stability of 1 ppm over industrial temperature range

NASA Astrophysics Data System (ADS)

You, Weilong; Pei, Binbin; Sun, Ke; Zhang, Lei; Yang, Heng; Li, Xinxin

2017-10-01

This paper presents an oven controlled N++ [1 0 0] length-extensional mode silicon resonator, with a lookup-table based control algorithm. The temperature coefficient of resonant frequency (TCF) of the N++ doped resonator is nonlinear, and there is a turnover temperature point at which the TCF is equal to zero. The resonator is maintained at the turnover point by Joule heating; this temperature is a little higher than the upper limit of the industrial temperature range. It is demonstrated that the control algorithm based on the thermoresistor on the substrate and the lookup table for heating voltage versus chip temperature is sufficiently accurate to achieve a frequency stability of  ±0.5 ppm over the industrial temperature range. Because only two leads are required for electrical heating and piezoresistive sensing, the power required for heating of this resonator can be potentially lower than that of the oscillators with closed-loop oven control algorithm. It is also shown that the phase noise can be suppressed at the turnover temperature because of the very low value of the TCF, which justifies the usage of the heating voltage as the excitation voltage of the Wheatstone half-bridge.

Evaluation of the boson chemiluminescence immunoassay as a first-line screening test in the ECDC algorithm for syphilis serodiagnosis in a population with a high prevalence of syphilis.

PubMed

Qiu, Xin-Hui; Zhang, Ya-Feng; Chen, Yu-Yan; Zhang, Qiao; Chen, Fu-Yi; Liu, Long; Fan, Jin-Yi; Gao, Kun; Zhu, Xiao-Zhen; Zheng, Wei-Hong; Zhang, Hui-Lin; Lin, Li-Rong; Liu, Li-Li; Tong, Man-Li; Zhang, Chang-Gong; Niu, Jian-Jun; Yang, Tian-Ci

2015-04-01

We developed a new Boson chemiluminescence immunoassay (CIA) and evaluated its application with cross-sectional analyses. Our results indicated that the Boson CIA demonstrated strong discriminatory power in diagnosing syphilis and that it can be used as a first-line screening test for syphilis serodiagnosis using the European Centre for Disease Prevention and Control algorithm or as a confirmatory test when combined with a patient's clinical history. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Neural learning of constrained nonlinear transformations

NASA Technical Reports Server (NTRS)

Barhen, Jacob; Gulati, Sandeep; Zak, Michail

1989-01-01

Two issues that are fundamental to developing autonomous intelligent robots, namely, rudimentary learning capability and dexterous manipulation, are examined. A powerful neural learning formalism is introduced for addressing a large class of nonlinear mapping problems, including redundant manipulator inverse kinematics, commonly encountered during the design of real-time adaptive control mechanisms. Artificial neural networks with terminal attractor dynamics are used. The rapid network convergence resulting from the infinite local stability of these attractors allows the development of fast neural learning algorithms. Approaches to manipulator inverse kinematics are reviewed, the neurodynamics model is discussed, and the neural learning algorithm is presented.

Numerical Algorithms for Acoustic Integrals - The Devil is in the Details

NASA Technical Reports Server (NTRS)

Brentner, Kenneth S.

1996-01-01

The accurate prediction of the aeroacoustic field generated by aerospace vehicles or nonaerospace machinery is necessary for designers to control and reduce source noise. Powerful computational aeroacoustic methods, based on various acoustic analogies (primarily the Lighthill acoustic analogy) and Kirchhoff methods, have been developed for prediction of noise from complicated sources, such as rotating blades. Both methods ultimately predict the noise through a numerical evaluation of an integral formulation. In this paper, we consider three generic acoustic formulations and several numerical algorithms that have been used to compute the solutions to these formulations. Algorithms for retarded-time formulations are the most efficient and robust, but they are difficult to implement for supersonic-source motion. Collapsing-sphere and emission-surface formulations are good alternatives when supersonic-source motion is present, but the numerical implementations of these formulations are more computationally demanding. New algorithms - which utilize solution adaptation to provide a specified error level - are needed.

An Information Theoretic Framework and Self-organizing Agent- based Sensor Network Architecture for Power Plant Condition Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Loparo, Kenneth; Kolacinski, Richard; Threeanaew, Wanchat

A central goal of the work was to enable both the extraction of all relevant information from sensor data, and the application of information gained from appropriate processing and fusion at the system level to operational control and decision-making at various levels of the control hierarchy through: 1. Exploiting the deep connection between information theory and the thermodynamic formalism, 2. Deployment using distributed intelligent agents with testing and validation in a hardware-in-the loop simulation environment. Enterprise architectures are the organizing logic for key business processes and IT infrastructure and, while the generality of current definitions provides sufficient flexibility, the currentmore » architecture frameworks do not inherently provide the appropriate structure. Of particular concern is that existing architecture frameworks often do not make a distinction between ``data'' and ``information.'' This work defines an enterprise architecture for health and condition monitoring of power plant equipment and further provides the appropriate foundation for addressing shortcomings in current architecture definition frameworks through the discovery of the information connectivity between the elements of a power generation plant. That is, to identify the correlative structure between available observations streams using informational measures. The principle focus here is on the implementation and testing of an emergent, agent-based, algorithm based on the foraging behavior of ants for eliciting this structure and on measures for characterizing differences between communication topologies. The elicitation algorithms are applied to data streams produced by a detailed numerical simulation of Alstom’s 1000 MW ultra-super-critical boiler and steam plant. The elicitation algorithm and topology characterization can be based on different informational metrics for detecting connectivity, e.g. mutual information and linear correlation.« less

Fast algorithm of low power image reformation for OLED display

NASA Astrophysics Data System (ADS)

Lee, Myungwoo; Kim, Taewhan

2014-04-01

We propose a fast algorithm of low-power image reformation for organic light-emitting diode (OLED) display. The proposed algorithm scales the image histogram in a way to reduce power consumption in OLED display by remapping the gray levels of the pixels in the image based on the fast analysis of the histogram of the input image while maintaining contrast of the image. The key idea is that a large number of gray levels are never used in the images and these gray levels can be effectively exploited to reduce power consumption. On the other hand, to maintain the image contrast the gray level remapping is performed by taking into account the object size in the image to which each gray level is applied, that is, reforming little for the gray levels in the objects of large size. Through experiments with 24 Kodak images, it is shown that our proposed algorithm is able to reduce the power consumption by 10% even with 9% contrast enhancement. Our algorithm runs in a linear time so that it can be applied to moving pictures with high resolution.