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1、AggregateStaticLoadModelinginPowerGridwithEnvironmentalCharacteristicsAOPeiMULong-huaSchoolofElectronicsandInformationEngineering,SchoolofElectronicsandInformationEngineering,TongjiUniversity,Shanghai201804,ChinaTongjiUniversity,Shanghai201804,ChinaE-mail:aopei16.student@sina.comE-mail:l
2、hmu@vip.163.comAbstract-Inpractice,therearemanydefectsthatAnewclusteringmethodbasedonimprovedPSOundergroundloadmodelisbuiltbyusingthetraditionalalgorithmispresentedinthispaper.Thisclusteringstaticloadmodelincoalmine.Toenhancetheaccuracyofmethodisusedtoclassifytheloaddatainordertoreducelo
3、admodel,anewclusteringmethodbasedonimprovedthenumberofloadmodelbeforemodeling.Then,RBFPSOalgorithmispresentedinthispaper.ThisnewneuralnetworkbasedonimprovedPSOalgorithmisclusteringmethodisusedtoclassifytheloaddatainorderproposedtoestablishaggregateloadmodel.Dataofatoreducethenumberofload
4、modelbeforemodeling.Then,monitoringpointinaminingareaistakenastheobjectofRBFneuralnetworkbasedonimprovedPSOalgorithmisstudyinthispaper.Verifiedbytheresultsofsimulation,proposedtoestablishaggregateloadmodel.Verifiedbyancomparedwithtraditionalstaticloadmodel,themethodexample,comparedwithtr
5、aditionalstaticloadmodel,theproposedinthispapercangreatlyimprovetheaccuracymethodinthispapercangreatlyimprovetheaccuracyofofthemodel.themodel.Keywords-aggregatestaticloadmodel;particleswarm.CLUSTERINGMETHODBASEDIMPROVEDPSOclusteringalgorithm;subtractiveclusteringalgorithm;ALGORITHMK-mean
6、sclusteringalgorithm;radialbasisneuralnetworkA.SubtractiveClusteringAlgorithm.INTRODUCTIONSubtractiveclusteringalgorithm[1,2]isasimpleandrelativelyefficientclusteringalgorithm.ComparedwithUndergroundpowergridincoalmineisaspecialtheotherclusteringalgorithm,thenumberofclustersbranchofthepo
7、wersystem.Itsoperatingenvironmentisneednotbepre-determinedbyusingsubtractiveseverebecauseitisimpactedbygeologicalconditions,clusteringalgorithm.Andclustercenterisdeterminedbygasandotherfactors.Inthepast,undergroundaggregatethedensityindexbasedonsampledatamainly.staticload
