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基于模糊神经网络的风电场无功补偿容量研究.docx

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基于模糊神经网络的风电场无功补偿容量研究 Abstract： Asthedevelopmentofwindpowergeneration,thereactivepowercompensationsystemhasbecomeanimportantpartofwindfarms.Inordertoimprovethecontrolaccuracyandefficiencyofthereactivepowercompensationsystem,afuzzyneuralnetworkbasedreactivepowercompensationcapacityresearchisproposed.Thispaperfirstintroducesthebasicconceptsofreactivepowercompensation,andthenpresentsthestructureandworkingprincipleofthefuzzyneuralnetwork.Theapplicationofthefuzzyneuralnetworkinreactivepowercompensationsystemisanalyzedindetail.Finally,experimentswerecarriedouttoverifytheeffectivenessoftheproposedmodel. Keywords:windpowergeneration,reactivepowercompensation,fuzzyneuralnetwork Introduction： Withthedevelopmentofwindpowergeneration,windfarmshavebecomeanimportantsourceofrenewableenergy.However,windpowergenerationisaffectedbyvariousfactorssuchaswindspeed,winddirection,andtemperature,whichleadstofluctuationsintheoutputpowerofwindturbines.Atthesametime,duetothecharacteristicsofvariablefrequencyandvariablespeed,windpowergenerationhasacertainimpactonthepowergrid,especiallyonthereactivepowerofthepowergrid.Therefore,thereactivepowercompensationsystemhasbecomeanimportantpartofwindfarms. Reactivepowercompensationistobalancethereactivepowerofthepowersystemandimprovethepowerfactor,whichcanreducethelossofthetransmissionanddistributionsystem,improvethevoltagequality,andstabilizethepowersystem.Themainmethodsofreactivepowercompensationarereactivepowercompensationequipment,reactivepowercompensationdevices,andreactivepowercompensationcapacity.Amongthem,reactivepowercompensationcapacitycanadjusttheoutputvoltageofthegeneratortoachievethepurposeofreactivepowercompensation. Fuzzyneuralnetworkisacombinationoffuzzylogicandneuralnetwork,whichhasgoodperformanceinsystemidentification,control,modeling,andotherfields.Fuzzyneuralnetworkcanperformfuzzyreasoningandadaptivelearning,whichcaneffectivelydealwithuncertainsystemsandcomplexenvironments.Therefore,theapplicationoffuzzyneuralnetworkinreactivepowercomp