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A dynamic recurrent neural network for wide area identification of a multimachine power system with a FACTS device

A dynamic recurrent neural network for wide area identificat...

引用

13th International Conference on Intelligent systems Application to Power systems, ISAP'05

作者： Mohagheghi, S. Venayagamoorthy, G.K. Harley, R.G. IEEE School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 United States Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO 65409-0249 United States University of KwaZulu-Natal South Africa

ISBN: (纸本)1599750287

Multilayer perceptron and radial basis function neural networks have been traditionally used for plant identification in power systems applications of neural networks. While being efficient in tracking the plant dynamics in a relatively small system, their performance degrades as the dimensions of the plant to be identified are increased, for example in supervisory level identification of a multimachine power system for Wide Area Control purposes. Recurrent neural networks can deal with such a problem by modeling the system as a set of differential equations and with less order of complexity. Such a recurrent neural network identifier is designed and implemented for supervisory level identification of a multimachine power system with a FACTS device. Simulation results are provided to show that the neuroidentifier can track the system dynamics with sufficient accuracy. © 2005 ISAP.

关键词： Electric power systems

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real-time dual heuristic program-ming-based neurocontroller for a turbogenerator in a multimachine power system

Real-time dual heuristic program-ming-based neurocontroller ...

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作者： Venayagamoorthy, Ganesh K. Harle, Ronald G. Wunsch, Donald C. Real-Time Power and Intelligent Systems Laboratory University of Missouri Rolla MO 65409 United States Department of Electrical and Computer Engineering University of Missouri Rolla MO 65409 United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 United States

Based on Dual Heuristic Programming (DHP), a real-time implementation of a neurocontroller for excitation and turbine control of a turbogenerator in a multimachine power system is presented. The feedback variables are completely based on local measurements. Simulation and real-time hardware implementation on a three-machine system demonstrate that the DHP neurocontroller is much more effective than conventional PID controllers, the automatic voltage regulator, power system stabilizer and the governor, for improving dynamic performance and stability under small and large disturbances. © 2005 CRL Publishing Ltd.

关键词： Heuristic programming

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Distributed mining of partial periodic patterns in sequences

Distributed mining of partial periodic patterns in sequences

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作者： Hsiao, Han-Wen Tsai, Meng-Shu Tsai, Jeffrey J. P. Department of Computer Science and Information Engineering Asia University Taiwan Taiwan Institute of Information Technology Asia University Taiwan Taiwan Department of Computer Science University of Illinois Chicago United States Taichung Healthcare and Management University Department of Computer Science and Information Engineering Department of Biotechnology and Bioinformatics Department of Electrical Engineering and Computer Science University of Illinois Chicago Distributed Real-Time Intelligent Systems Laboratory

It has been an important task of discovering frequent fragments as particular patterns from large sequence databases generated from a variety of applications. In general, the patterns to be discovered may partially and asynchronously exist in sequences, and even contain gaps. In addition, it is necessary to collect the information regarding the locations and frequencies of the patterns. How to enumerate candidate patterns for evaluation without exponentially increasing the computation is another problem. In this paper, the modified periodicity transform is proposed to meet the requirements mentioned above. Also, a distributed computing framework is implemented to perform the mining task more efficiently. Both synthetic and biological sequences are utilized to examine the approach. The experimental results demonstrate the efficiency and effectiveness the system.

关键词： Data mining

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A neural network based Wide Area Monitor for a power system

A neural network based Wide Area Monitor for a power system

引用

2005 IEEE Power Engineering Society General Meeting

作者： Li, Xiaomeng Venayagamoorthy, Ganesh K. IEEE Real-Time Power and Intelligent Systems Laboratory University of Missouri Rolla MO 65409 United States Department of Electrical and Computer Engineering University of Missouri Rolla United States South African Institute of Electrical Engineers IEEE St. Louis Computational Intelligence Department of Task Force on Intelligent Control Systems Secretary of the Intelligent Systems Subcommittee IEEE Power Engineering Society

ISBN: (纸本)078039156X

With the deregulation of power industry, many tie lines between control areas are driven to operate near their maximum capacity, especially those serving heavy load centers. Wide area controllers (WACs) using wide-area or global signals can provide remote auxiliary control signals to local controllers such as automatic voltage regulators, power system stabilizers, etc to damp out inter-area oscillations. The power system is highly nonlinear system with fast changing dynamics. In order to have an efficient WAC, an online system monitor/predictor is required to provide inter-area information to the WAC from time to time. This paper presents the design of an online Wide Area Monitor (WAM) using a neural network called the Wide Area Neuroidentifier (WANI). The WANI is used to predict ahead the speed deviations of generators in the different areas using phasor measurement unit (PMU). Results are presented to show the effectiveness of the wide area monitor for different disturbances. © 2005 IEEE.

关键词： Wide area networks

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A dynamic recurrent neural network for wide area identification of a multimachine power system with a FACTS device

A dynamic recurrent neural network for wide area identificat...

引用

International Conference on Intelligent systems Applications to Power systems (ISAP)

作者： S. Mohagheghi G.K. Venayagamoorthy R.G. Harley Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta GA USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA

Multilayer perceptron and radial basis function neural networks have been traditionally used for plant identification in power systems applications of neural networks. While being efficient in tracking the plant dynamics in a relatively small system, their performance degrades as the dimensions of the plant to be identified are increased, for example in supervisory level identification of a multimachine power system for wide area control purposes. Recurrent neural networks can deal with such a problem by modeling the system as a set of differential equations and with less order of complexity. Such a recurrent neural network identifier is designed and implemented for supervisory level identification of a multimachine power system with a FACTS device. Simulation results are provided to show that the neuroidentifier can track the system dynamics with sufficient accuracy

关键词： Power system dynamics Recurrent neural networks Power systems Power system modeling Power system simulation Multilayer perceptrons Radial basis function networks Neural networks Multi-layer neural network Degradation

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A novel method for predicting harmonic current injection from non-linear loads using neural networks

A novel method for predicting harmonic current injection fro...

引用

Annual Conference of Industrial Electronics Society

作者： J. Mazumdar R. Harley F. Lambert G.K. Venayagamoorthy School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla Rolla MA USA

Generation of harmonics and the existence of waveform pollution in power system networks is one of the major problems facing the utilities. This paper proposes a neural network solution methodology for the problem of measuring the actual amount of harmonic current injected into a power network by a nonlinear load. The determination of harmonic currents is complicated by the fact that the supply voltage waveform is distorted by other loads and is rarely a pure sinusoid. A recurrent neural network trained with the backpropagation through time (BPTT) training algorithm is used to find a way of distinguishing between the load harmonics and supply harmonics, without disconnecting the load from the network. The biggest advantage of this method is that only waveforms of voltages and currents have to be measured. This method is applicable for both single and three phase loads. This technology could be fabricated into a commercial instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument

关键词： Neural networks Power system harmonics Current measurement Pollution measurement Voltage Instruments Power generation Power system measurements Distortion measurement Power measurement

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Using a Neural Network to Distinguish Between the Contributions to Harmonic Pollution of Non-Linear Loads and the Rest of the Power System

Using a Neural Network to Distinguish Between the Contributi...

引用

Annual IEEE Conference on Power Electronics Specialists (PESC)

Harmonics are one of the important power quality measurable quantities. This paper proposes a neural network solution methodology for the problem of measuring the actual amount of harmonic current injected into a power network by a non-linear load. The determination of harmonic currents is complicated by the fact that the supply voltage waveform is distorted by other loads and is rarely a pure sinusoid. Harmonics may therefore be classified as contributions from the load on the one hand and contributions from the power system or supply harmonics on the other hand. A recurrent neural network architecture based method is used to find a way of distinguishing between the load contributed harmonics and supply harmonics, without disconnecting the load from the network. The main advantage of this method is that only waveforms of voltages and currents have to be measured. This method is applicable for both single and three phase loads. This could be fabricated into a commercial instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument

关键词： Power system harmonics Neural networks Pollution measurement Distortion measurement Power measurement Current measurement Voltage Instruments Power system measurements Power quality

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Adaptive critic designs for optimal control of power systems

Adaptive critic designs for optimal control of power systems

引用

International Conference on Intelligent systems Applications to Power systems (ISAP)

作者： G.K. Venayagamoorthy R.G. Harley Real-Time Power and Intelligent Systems (RTPIS) Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

The increasing complexity of the modern power grid highlights the need for advanced modeling and control techniques for effective control of excitation, turbine and flexible AC transmission systems (FACTS). The crucial factors affecting the modern power systems today is voltage and load flow control. Simulation studies in the PSCAD/EMTDC environment and realtime laboratory experimental studies carried out are described and the results show the successful control of the power system elements and the entire power system with adaptive and optimal neurocontrol schemes. Performances of the neurocontrollers are compared with the conventional PI controllers for damping under different operating conditions for small and large disturbances

关键词： Programmable control Adaptive control Optimal control Power system control Power systems Power system simulation PSCAD Power system modeling Flexible AC transmission systems Power grids

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Hardware implementation of a Mamdani fuzzy logic controller for a static compensator in a multimachine power system

Hardware implementation of a Mamdani fuzzy logic controller ...

引用

Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： S. Mohagheghi G.K. Venayagamoorthy S. Rajagopalan R.G. Harley School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla Rolla MO USA

A Mamdani based fuzzy logic controller is designed and implemented for controlling a STATCOM, which is connected to a 10 bus multimachine power system. Such a controller does not need any prior knowledge of the plant to be controlled and can efficiently provide control signals for the STATCOM during different disturbances in the network The proposed controller is implemented using the M67 DSP board and is interfaced to the multimachine power system simulated on a real-time digital simulator (RTDS). Experimental results are provided, showing that the proposed controller provides more effective damping than the conventional PI controller in a typical large scale disturbance, i.e., a three phase short circuit.

关键词： Hardware Fuzzy logic Control systems STATCOM Power systems Power system simulation Automatic voltage control Circuit simulation Control system synthesis Digital signal processing

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Benchmarking for graph transformation

Benchmarking for graph transformation

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IEEE Symposium on Visual Languages and Human Centric Computing (VL/HCC)

作者： G. Varro A. Schurr D. Varro Department of Computer Science and Information Theory Budapest University슠of슠Technology슠and슠Economics Budapest Hungary Real-Time Systems Lab Data Systems Technology Institute Technical University of Darmstadt Darmstadt Germany

Model transformation (MT) is a key technology in the model-driven development approach of software engineering that provides automated means to capture the evolution of models and mappings between modeling languages. The pattern and rule-based paradigm of graph transformation is considered a very popular approach for specifying such model transformations. While the expressiveness of different MT specification techniques is frequently compared on well-known transformation problems (e.g. UML-to-XMI, or UML-to-EJB mappings), no such benchmarks exist currently for comparing the performance of different model transformation tools. In the paper, we propose a systematic method for quantitative benchmarking in order to assess the performance of graph transformation tools. Typical features of the graph transformation paradigm and various optimization strategies exploited in different toots are identified and categorized. Moreover, the performance of several popular graph transformation tools is measured and compared on a well-known distributed mutual exclusion problem.

关键词： Software engineering real time systems Data systems Information systems computer architecture Microwave integrated circuits Modeling Productivity Automation Civil engineering

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