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Title: Adaptive neural network based stabilizing controller design for single machine infinite bus power systems
Author (s): Liu, Wenxin
Sarangapani, Jagannathan
Venayagamoorthy, Ganesh K.
Wunsch, Donald C.
Crow, Mariesa L.
Cartes, David A.
Department/Lab Affiliations: Applied Computational Intelligence Laboratory
Computer Science
Electrical and Computer Engineering
Energy Research and Development Center
Engineering Management & Systems Engineering
Intelligent Systems Center
Keywords: particle swarm optimization
power systems
stabilizing control
Subject Terms: Neural networks (Computer science)
Issue Date: 2007
Publisher: Watam Press
Citation: Liu, Wenxin, Sarangapani Jagannathan, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa L. Crow and David A. Cartes. “Adaptive Neural Network Based Stabilizing Controller Design for Single Machine Infinite Bus Power Systems.” Dynamics of Continuous, Discrete and Impulse Systems, Series A, vol. 14 (S1), pp. 494-502, 2007.
Abstract: Power system stabilizers are widely used to generate supplementary control signals for the excitation system in order to damp out the low frequency oscillations. In power system control literature, the performances of the proposed controllers were mostly demonstrated using simulation results without any rigorous stability analysis. This paper proposes a stabilizing neural network (NN) controller based on a sixth order single machine infinite bus power system model. The NN is used to approximate the complex nonlinear dynamics of power system. Unlike the other indirect adaptive NN control schemes, there is no offline training process and the NN can be directly used online and learn through time. Magnitude constraint of the activators is modeled as saturation nonlinearities and is included in the Lyapunov stability analysis. The new NN controller design is compared with conventional power system stabilizers (CPSS) whose parameters are fine tuned by particle swarm optimization (PSO). Simulation results demonstrate that the proposed NN controller design can successfully damp out power system oscillations. The control algorithms of this paper can also be applied to other similar nonlinear control problems.
Type: Article - Journal
text
In Title: Dynamics of Continuous, Discrete and Impulse Systems, Series A.
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titleAdaptive neural network based stabilizing controller design for single machine infinite bus power systems
contributor.authorLiu, Wenxin
contributor.authorSarangapani, Jagannathan
contributor.authorVenayagamoorthy, Ganesh K.
contributor.authorWunsch, Donald C.
contributor.authorCrow, Mariesa L.
contributor.authorCartes, David A.
contributor.deptlabApplied Computational Intelligence Laboratory
contributor.deptlabComputer Science
contributor.deptlabElectrical and Computer Engineering
contributor.deptlabEnergy Research and Development Center
contributor.deptlabEngineering Management & Systems Engineering
contributor.deptlabIntelligent Systems Center
subjectparticle swarm optimization
subjectpower systems
subjectstabilizing control
subject.LCSHNeural networks (Computer science)
date.issued2007
publisherWatam Press
identifier.citationLiu, Wenxin, Sarangapani Jagannathan, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa L. Crow and David A. Cartes. “Adaptive Neural Network Based Stabilizing Controller Design for Single Machine Infinite Bus Power Systems.” Dynamics of Continuous, Discrete and Impulse Systems, Series A, vol. 14 (S1), pp. 494-502, 2007.
identifier.pub.URI
http://www.watam.org/DCDIS_supp/contents-part1.pdf
description.abstractPower system stabilizers are widely used to generate supplementary control signals for the excitation system in order to damp out the low frequency oscillations. In power system control literature, the performances of the proposed controllers were mostly demonstrated using simulation results without any rigorous stability analysis. This paper proposes a stabilizing neural network (NN) controller based on a sixth order single machine infinite bus power system model. The NN is used to approximate the complex nonlinear dynamics of power system. Unlike the other indirect adaptive NN control schemes, there is no offline training process and the NN can be directly used online and learn through time. Magnitude constraint of the activators is modeled as saturation nonlinearities and is included in the Lyapunov stability analysis. The new NN controller design is compared with conventional power system stabilizers (CPSS) whose parameters are fine tuned by particle swarm optimization (PSO). Simulation results demonstrate that the proposed NN controller design can successfully damp out power system oscillations. The control algorithms of this paper can also be applied to other similar nonlinear control problems.
typeArticle - Journal
type.DCMITypetext
type.statusFinal version
rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
rightsAllows final version
rights.URI
http://watam.org/
relation.isPartOfDynamics of Continuous, Discrete and Impulse Systems, Series A.
date.accessioned2008-07-22T19:26:08Z
date.available2008-07-29T16:03:10Z
identifier.persist.URI
http://scholarsmine.mst.edu/post_prints/AdaptiveNeuralNetworkBasedStabilizingController_09007dcc8053c327.html
Full Text
AdaptiveNeuralNetworkBased_09007dcc8053c702.pdf