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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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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 ...

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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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Adaptive critic designs for optimal control of power systems

Adaptive critic designs for optimal control of power systems

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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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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...

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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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A retraining methodology for enhancing agent intelligence

A retraining methodology for enhancing agent intelligence

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International Conference on Integration of Knowledge Intensive Multi-Agent systems (KIMAS)

作者： P.A. Mitkas A.L. Symeonidis I.N. Athanasiadis Intelligent Systems and Software Engineering Laboratory Informatics and Telematics Institute CERTH Thessaloniki Greece Electrical and Computer Engineering Department Aristotle University of Thessaloniki Thessaloniki Greece

Data mining has proven a successful gateway for discovering useful knowledge and for enhancing business intelligence in a range of application fields. Incorporating this knowledge into already deployed applications, though, is highly impractical, since it requires reconfigurable software architectures, as well as human expert consulting. In an attempt to overcome this deficiency, we have developed agent academy, an integrated development framework that supports both design and control of multiagent systems (MAS), as well as agent training. We define agent training as the automated incorporation of logic structures generated through data mining into the agents of the system. The increased flexibility and cooperation primitives of MAS, augmented with the training and retraining capabilities of agent academy, provide a powerful means for the dynamic exploitation of data mining extracted knowledge. In this paper, we present the methodology and tools for agent retraining. Through experimental results with the agent academy platform, we demonstrate how the extracted knowledge can be formulated and how retraining can lead to the improvement - in the long run - of agent intelligence.

关键词： intelligent agent Delta modulation Data mining Humans Companies Application software Software architecture Multiagent systems Reconfigurable logic Collaborative software

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Monotonic convergent iterative learning controller design with iteration varying model uncertainty

Monotonic convergent iterative learning controller design wi...

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IEEE International Conference on Mechatronics and Automation, ICMA 2005

作者： Ahn, Hyo-Sung Moore, Kevin L. Chen, YangQuan Dept. of Electrical and Computer Engineering Utah State University Logan UT 84322-4160 United States Research and Technology Development Center Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Road Laurel MD 20723-6099 United States Center for Self-Organizing and Intelligent Systems Dept. of Electrical and Computer Engineering Utah State University 4160 Old Main Hill Logan UT 84322-4160

ISBN: (纸本)0780390458

In iterative learning control, model uncertainty issue has been studied in various ways. But, in existing works, the model uncertainty has been considered as iteration invariant, that is, it does not change on the iteration axis. Furthermore, in these works, monotone convergence problem has not been properly studied. In this paper, new monotonic convergence condition of iteration varying model uncertain iterative learning control system is derived. So, the main goal of this paper is to guarantee the monotone convergence of the iteration varying model uncertainty system until the tracking error is reduced below the previously calculated base-line error boundary. Through the numerical example, the validity of the suggested method is illustrated. © 2005 IEEE.

关键词： Control equipment

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Schur stability radius bounds for robust iterative learning controller design

Schur stability radius bounds for robust iterative learning ...

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2005 American Control Conference, ACC

作者： Ahn, Hyo-Sung Moore, Kevin L. Chen, YangQuan Dept. of Electrical and Computer Engineering Utah State University 4160 Old Main Hill Logan UT 84322-4160 United States Research and Technology Development Center Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Road Laurel MD 20723-6099 Center for Self-Organizing and Intelligent Systems Dept. of Electrical and Computer Engineering Utah State University 4160 Old Main Hill Logan UT 84322-4160

This paper computes bounds on the Schur stability radius for use in designing robust iterative learning controllers. The discrete Lyapunov equation is used to compute the Schur stability radius of the ILC system for the case of parametric or interval perturbations in the system Markov parameters. This paper is distinct from the existing ILC works in that the interval robustness concepts are integrated with asymptotic stability and monotonie convergence conditions to suggest design of the learning gain matrix that makes the system robust against interval model uncertainties up to a computable bound. After derivations of the analytical stability radius, optimization schemes are suggested to design the learning gain matrix. The proposed approach allows design of causal/noncausal time-varying learning matrix gains. © 2005 AACC.

关键词： Control equipment

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A novel approach to proactive human-robot cooperation

A novel approach to proactive human-robot cooperation

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： O.C. Schrempf U.D. Hanebeck A.J. Schmid H. Worn Intelligent Sensor-Actuator-Systems Laboratory Institute of Computer Science and Engineering Universität Karlsruhe Karlsruhe Germany Institute for Process Control and Robotics Universität Karlsruhe Karlsruhe Germany

This paper introduces the concept of proactive execution of robot tasks in the context of human-robot cooperation with uncertain knowledge of the human's intentions. We present a system architecture that defines the necessary modules of the robot and their interactions with each other. The two key modules are the intention recognition that determines the human user's intentions and the planner that executes the appropriate tasks based on those intentions. We show how planning conflicts due to the uncertainty of the intention information are resolved by proactive execution of the corresponding task that optimally reduces the system's uncertainly. Finally, we present an algorithm for selecting this task and suggest a benchmark scenario.

关键词： Robot sensing systems intelligent robots Uncertainty Application software Humanoid robots Human robot interaction Bayesian methods intelligent sensors Laboratories computer science

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Optimal dynamic neurocontrol of a gate-controlled series capacitor in a multi-machine power system

Optimal dynamic neurocontrol of a gate-controlled series cap...

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

作者： S. Ray G.K. Venayagamoorthy E.H. Watanabe F.D. de Jesus Real-Time Power and Intelligent Systems (RTPIS) Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA Department of Electrical Engineering Federal University of Rio Grande do Sul Brazil

This paper presents the design of an optimal dynamic neurocontroller for a new type of FACTS device - the gate controlled series capacitor (GCSC) incorporated in a multi-machine power system. The optimal neurocontroller is developed based on the heuristic dynamic programming (HDP) approach. In addition, a dynamic identifier/model and controller structure using the recurrent neural network trained with backpropagation through time (BPTT) is employed. Simulation results are presented to show the effectiveness of the dynamic neurocontroller and its performance is compared with that of the conventional PI controller under small and large disturbances

关键词： Power system dynamics Capacitors Power systems Neurocontrollers Power system modeling Power system simulation Optimal control Control systems Dynamic programming Recurrent neural networks

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