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基于ANN伪量测建模的配电网状态估计.docx

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基于ANN伪量测建模的配电网状态估计 Title:PowerDistributionNetworkStateEstimationbasedonArtificialNeuralNetworkPseudo-MeasurementModeling Abstract: Withtheincreasingcomplexityofpowerdistributionnetworks,accurateandreal-timeestimationoftheirstateiscrucialforefficientoperationandmanagement.Traditionalstateestimationapproaches,suchastheKalmanfilterorweightedleastsquares,relyheavilyontheaccuracyofthemeasurementdata,whichmaynotalwaysbereadilyavailable.Inthispaper,weproposeanovelstateestimationmethodbasedontheutilizationofartificialneuralnetworks(ANN)intheformofpseudo-measurements.BytrainingtheANNmodelusinghistoricaldata,theproposedmethodcaneffectivelyestimatethestatevariablesofthepowerdistributionnetworkevenwhenunreliableorinsufficientmeasurementsarepresent.TheperformanceandeffectivenessoftheANN-basedstateestimationapproachareevaluatedthroughsimulationsonatestdistributionnetwork,demonstratingitssuperiorcapabilitycomparedtotraditionalmethods. Introduction: Powerdistributionnetworksplayacriticalroleindeliveringelectricityfromtransmissionsubstationstoendconsumers.Theaccurateestimationoftheirstatevariablesisessentialforoptimaloperation,faultdetection,andsystemcontrol.Stateestimationtechniqueshavebeenwidelyusedtoestimatethevoltagemagnitudesandanglesatthenetworkbusesbasedontheavailablemeasurements,suchasmeasurementsfromphasormeasurementunits(PMUs),supervisorycontrolanddataacquisition(SCADA)systems,andsmartmeters.However,thesemeasurementsaresubjecttovariousuncertaintiesandlimitations,makingitchallengingtoachieveaccuratestateestimationsolelybasedontheavailablemeasurements. Toovercometheselimitations,anovelapproachbasedonartificialneuralnetworks(ANN)isproposed.ANNmodelshaveshowngreatpotentialinsolvingcomplexproblems,includingelectricalpowersystemapplications.Inthiswork,anANNisutilizedtomodelpseudo-measurements,whichrepresenttherelationshipsbetweenavailablemeasurementsandstatevariables.BytrainingtheANNmodelusinghistoricaldata,theproposedmethodcanestimatethestatevariableseveninthepresenceofunreliab