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基于模糊神经网络的MCR电压无功控制.docx

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基于模糊神经网络的MCR电压无功控制 Abstract Withtheincreasingdemandforcleanenergyproduction,theintegrationofrenewableenergysourcesintotheelectricgridhasbecomemorecommon.Oneofthekeychallengesinthisintegrationisthemanagementofvoltageandreactivepower(VAr)inthepowersystem.ThispaperpresentsastudyontheuseoffuzzyneuralnetworksforMCRvoltageVArcontrol.TheproposedapproachisbasedonthecombinationofafuzzylogiccontrollerandaneuralnetworktoachieveimprovedcontroloverVAr.Theresultsofthestudydemonstratethattheproposedapproachachievesbetterperformancethantraditionalstaticandadaptivecontrolstrategies. Introduction Theintegrationofrenewableenergysourcessuchaswindandsolarpowerintotheelectricgridhasbecomemorecommoninrecentyears.Theprimaryadvantageofthesesourcesisthattheyusecleanandrenewableenergy,whichreducesgreenhousegasemissionsandpromotessustainabledevelopment.However,theintegrationalsoposesanumberofchallengestothepowersystem,includingvoltageandreactivepowermanagement.Voltageandreactivepowercontrolarecriticalinmaintainingthestabilityandreliabilityofthepowersystem.Onekeyaspectofthiscontrolisthemanagementofthemaximumcontinuousrating(MCR)voltageandreactivepower. ThispaperpresentsastudyontheuseoffuzzyneuralnetworksforMCRvoltageVArcontrol.Theproposedapproachcombinesafuzzylogiccontrollerwithaneuralnetworktoimprovecontrolperformance.Theremainderofthispaperisorganizedasfollows.Section2reviewssomerelatedworkonvoltageandreactivepowercontrol.Section3describesthefuzzyneuralnetworkapproachusedforMCRvoltageVArcontrol.Section4presentssimulationresults,andSection5concludesthework. RelatedWork Variouscontrolstrategieshavebeenproposedforvoltageandreactivepowercontrol.Traditionalapproachesincludeproportional-integral(PI)control,proportional-resonant(PR)control,andadaptivecontrol.PIandPRcontrolcanachievegoodperformanceundersteady-stateconditions,buttheymaynotbeabletorespondquicklytodynamicchangesinthepowersystem,suchassuddenloadchangesortheintegrationofrenewableenergysources.Adaptivecontrolhasbeenproposedtoaddressthispro