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1、RealTimeImplementationofanArtificialImmuneSystemBasedControllerforaDSTATCOMinanElectricShipPowerSystemPinakiMitra,StudentMember,IEEEandGaneshK.Venayagamoorthy,SeniorMember,IEEEReal-TimePowerandIntelligentSystemsLaboratoryMissouriUniversityofScienceandTechnology,Rolla,MO65409,USA.pm33d@mst
2、.edu&gkumar@ieee.orgAbstract—Anewadaptivecontrolstrategybasedon[5]presentdifferentcontrolstrategiesbasedonthemulti-levelArtificialImmuneSystem(AIS)foraDSTATCOMinanelectricinvertertopologiesofshuntcompensators.Attemptshavebeenshippowersystemispresentedinthispaper.DSTATCOMisamadetomaketheco
3、ntrollerrobustbyapplyingslidingmodeshuntcompensationdevice,whichcanbeusedtoimprovethecontrolstrategyasin[6]and[7].But,thesecontrolstrategiespowerqualityduringthepulsepowerrequirementsinanavalarenotadaptivetothesystemdynamics.Also,mostoftheshipboardsystem.TheroleofDSTATCOMcontrollerisveryc
4、onventionalcontrolschemesofDSTATCOMhaveseveralPIimportanttomeetthisobjective.Inthispaper,theDSTATCOMcontrollers.ThetuningofPIcontrollersisacomplextaskforcontrollerparametersarefirsttunedbyParticleSwarmthesystemshavingpowerelectronicsequipments.InordertoOptimization(PSO)technique,sothatitc
5、anprovideinnateovercometheseproblems,ComputationalIntelligence(CI)immunitytocommonsystemdisturbances.Then,theseoptimumtechniquescanbeused.TherearenotsomanyattemptsofparametersaremodifiedonlinebyanartificialimmunesystemusingCItechniquesinDSTATCOMcontrol.References[8](AIS),whichprovidesadap
6、tiveimmunitytounusualsystemand[9]arebasedonArtificialNeuralNetworks(ANNs).Indisturbances.Toevaluatetheperformanceoftheproposedcontrolstrategy,asimplifiedmodeloftheshipsystemconsisting[8],thePIcontrollersarereplacedbyanANNwhichistrainedofa45MVAmaingenerator,a5MVAauxiliarygeneratorandausing
7、abackpropagationalgorithm.But,thetrainingiscarried36MWpropulsionmotorissimulatedinareal-timeoutofflineandhencetheANNbasedcontrollerisnotadaptive.environment.TheeffectivenessofthePSOandAISbasedIn[9],anANNbasedreferencecurrentgeneratorisused,adaptivecontrollerisdemons
