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1、ANONLINEARVOLTAGECONTROLLERWITHDERIVATIVEADAPTIVECRITICSFORMULTIMACHINEPOWERSYSTEMS*+G.K.VenayagamoorthyMember,*R.G.HarleyFellow,IEEED.C.WunschSeniorMember,IEEEIEEESchoolofElectricalandcomputerEngineering'AppliedComputationalIntelligenceLaboratoryDepartmentofElectronicEngi
2、neering,GeorgiaInstituteofTechnologyAtlantaGA30332-Dept.ofEiectricalandcomputer~~~i~~~ri~~MLSultanTechikon,POBox1334Durban40000250USAUniversityofMissouri-RollaSouthAfricaron.harley@ee.gatech.eduMO65409-0249USAgkumar@ieee.orgdwunsch@ece.timr.edu'ElectricalEngineeringDepartm
3、entUniversityofNatalDurban4041SouthAfricaAbstruct-Basedonderivativeadaptivecritics,anovelnonlinearsystems[7,81and,withcontinuallyonlinetraining,theseoptimalvoltage/excitationcontrolformultimachinepowersystemismodelscantrackthedynamicsoftheturbogeneratorsystem,presented.The
4、feedbackvariablesarecompletelybasedonlocalthusyieldingadaptiveidentification.ANNcontrollershavemeasurements.Simulationsonathree-machinesystemdemonstratebeensuccessfidlyimplementedonsingleturbogeneratorsusingthatthenonlinearcontrollerismuchmoreeffectivethanconventionalANNid
5、entifiersandindirectfeedback[9-121.Moreover,ANNPIDcontrollerequippedwithapowersystemstabilizerforimprovingdynamicperformanceandstabilityundersmallandlargedisturbances.identificationofturbogeneratorsinamulti-machinepowersystemhasalsobeenreported[13,141.Keywords:PowerSystemS
6、tability,VoltageRegulation,NonlinearClearly,nonlinearcontrollersareneededfornonlinearsystems.OptimalControl,AdaptiveCritic,ArtificialNeuralNetworks.InthispaperamultimachinepowersystemsismodeledusingI.INTRODUCTIONanartificialneuralnetworkandusedinthedevelopmentofaPowersyste
7、mscontainingturbogeneratorsarelarge-scalenonlinearvoltagecontroller(NVC)basedonderivativenonlinearsystems.Thetraditionalexcitationcontrollersfortheadaptivecritics,toreplacethetraditionalautomaticvoltagegeneratorsaredesignedbylinearcontroltheorybasedonaregulator(AVR)andacon
8、ventionalpowersystemstabilizersingle-machineinfinitebus(SMIB)powersystemmodel.(PSS).Withd
