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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)

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 MO USA

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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Recognizing interaction from a robot's perspective

Recognizing interaction from a robot's perspective

引用

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： T. Salter F. Michaud K. Dautenhahn D. Letourneau S. Caron LABORIUS-Research Laboratory on Mobile Robotics and Intelligent Systems Department of Electrical Engineering and Computer Engineering Université de Sherbrook Sherbrooke QUE Canada Adaptive Systems Research Group School of Computer Science University of Herfordshire Hatfield UK

For meaningful interaction between a robot and a human, an autonomous robot must recognize whether the experienced situation is created by people or by the environment. Using only proprioceptive data from a mobile robotic platform, we discover that it is possible to distinguish sensory data patterns involving interaction. These patterns are obtained whilst navigating varying environments, both human populated and unpopulated. The paper reports the initial set of trials using Roball, a spherical mobile robot. Also described is the experimental methodology currently followed to validate the hypothesis that child interaction can be perceived directly from navigation sensors onboard a robotic platform.

关键词： Robot sensing systems Mobile robots Human robot interaction Navigation intelligent robots Sensor phenomena and characterization Infrared sensors Educational institutions Plastics Mobile computing

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Robust stability check of fractional order linear time invariant systems with interval uncertainties

Robust stability check of fractional order linear time invar...

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IEEE International Conference on Mechatronics and Automation

作者： YangQuan Chen Hyo-Sung Ahn I. Podlubny Center for Self-Organizing and Intelligent Systems CSOIS Department of Electrical and Computer Engineering Utah State University Logan UT USA Department of Informatization and Control of Processes Technical University of Kosice Kosice Slovakia

For uncertain fractional-order linear time invariant (FO-LTI) systems with interval coefficients described in state space form, the robust stability check problem is solved for the first time in this paper. Both the checking procedure and the Matlab code are presented with two illustrative examples. The conservatism is shown to be small.

关键词： Robust stability Time invariant systems Uncertainty Control systems Controllability System testing intelligent systems State-space methods Calculus Process control

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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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Power system optimization and coordination of damping controls by series FACTS devices

Power system optimization and coordination of damping contro...

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IEEE Power engineering Society Conference and Exposition in Africa, PowerAfrica

作者： Jung-Wook Park R.G. Harley G.K. Venayagamoorthy LG Electronics Inc. Seoul South Korea School of Electrical and Computer Engineering Georgia Institute of Technology USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA

Controllable series line compensation by series flexible ac transmission system (FACTS) devices can provide significant transient stability improvement and effective power oscillation damping. The work presented in this paper gives an overview of control issues of series FACTS devices contributing to stable power system operation when applying suitable controllers with appropriately selected system measurements. Firstly, this paper describes two internal control schemes, which are based on the automatic power flow control mode and line impedance compensation mode, for a switching converter type based series FACTS device. Secondly, it shows that the external (secondary) control can give a solution for overall system optimization by providing well-established supplementary controls and adaptive reference changes to the internal controller. Case studies are presented with simulation results by applying the proposed controllers to the series FACTS device on a power system

关键词： Power systems Damping Control systems Automatic control Power system stability Power system transients Flexible AC transmission systems Power system simulation Power system measurements Power system control

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An adaptive Mamdani fuzzy logic based controller for a static compensator in a multimachine power system

An adaptive Mamdani fuzzy logic based controller for a stati...

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

作者： S. Mohagheghi G.K. Venayagamoorthy 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 MO USA University of KwaZulu-Natal South Africa

An adaptive Mamdani based fuzzy logic controller has been designed for controlling a static compensator (STATCOM) in a multimachine power system. Such a controller does not need any prior knowledge of the plant to be controlled and can efficiently control a STATCOM during different disturbances in the network. A model free approach using the controller output error is applied for training purposes that adaptively changes the controller output parameters based on a gradient descent method. Moreover, shrinking span membership functions are used for a more stable and accurate control performance. Simulation results show that the proposed controller outperforms the conventional PI controller during dynamic and transient disturbances

关键词： Programmable control Adaptive control Fuzzy logic Control systems STATCOM Power systems Automatic voltage control Power system modeling Adaptive systems Error correction

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A comparison of PSO and backpropagation for training RBF neural networks for identification of a power system with STATCOM

A comparison of PSO and backpropagation for training RBF neu...

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IEEE Swarm Intelligence Symposium, SIS

作者： S. Mohaghegi Y. del Valle G.K. Venayagamoorthy 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 MO USA Professor Emeritus University of KwaZulu-Natal South Africa

Backpropagation algorithm is the most commonly used algorithm for training artificial neural networks. While being a straightforward procedure, it suffers from extensive computations, relatively slow convergence speed and possible divergence for certain conditions. The efficiency of this method as the training algorithm of a radial basis function neural network (RBFN) is compared with that of particle swarm optimization, for neural network based identification of a small power system with a static compensator. The comparison of the two methods is based on the convergence speed and robustness of each method.

关键词： Neural networks Power systems Automatic voltage control Artificial neural networks Backpropagation algorithms Convergence Radial basis function networks Particle swarm optimization STATCOM Robustness

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