BP神经网络在测试系统动态补偿中的应用(英文)-豆柴文库

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BP神经网络在测试系统动态补偿中的应用(英文)
ApplicationofBackpropagationNeuralNetworkinTestingSystemDynamicCompensation
Abstract:
Withthedevelopmentofpowersystems,therequirementforaccurateandreliabletestingofsystemdynamicsbecomesincreasinglyimportant.Thispaperintroducestheapplicationofbackpropagationneuralnetwork(BPNN)intestingsystemdynamiccompensation.TheBPNNmethodhasbeenwidelyusedinvariousfieldsduetoitsabilitytoapproximatenonlinearfunctionsandlearncomplexpatterns.Inthisstudy,BPNNisutilizedtocompensateforthesystemdynamicsduringtesting,aimingtoimprovetheaccuracyandreliabilityofthetestingresults.ThepaperdiscussesthetrainingprocessandthetestingprocedureoftheBPNN,aswellaspresentsacasestudytodemonstrateitseffectivenessinsystemdynamiccompensation.TheresultsshowthattheapplicationofBPNNintestingsystemdynamiccompensationcansignificantlyimprovetheaccuracyofthetestingprocessandenhancethereliabilityofthetestingresults.
1.Introduction
Powersystemsarecomplexdynamicsystemsthatrequireaccuratetestingtoensuretheirstableandreliableoperation.Systemdynamiccompensationisacrucialstepintestingpowersystems,asithelpstoeliminatetheeffectsofsystemdynamicsandensureaccuratemeasurementofthesystemresponse.Traditionalcompensationmethodsoftenrelyonmathematicalmodelsandmanualtuning,whichcanbetime-consumingandpronetoerrors.Inrecentyears,artificialintelligencetechniques,especiallyneuralnetwork-basedapproaches,haveshowngreatpotentialinsystemdynamiccompensation.
2.BackpropagationNeuralNetwork
2.1StructureandPrinciple
Thebackpropagationneuralnetwork(BPNN)isamultilayerfeedforwardneuralnetworkthatconsistsofaninputlayer,oneormorehiddenlayers,andanoutputlayer.Itutilizesasupervisedlearningalgorithm,wherethenetworkistrainedusingalabeleddataset.TheprincipleofBPNNistoiterativelyadjusttheweightsandbiasesoftheneuronsinthenetworktominimizethedifferencebetweentheactualoutputandthedesiredoutput.
2.2TrainingProcess
ThetrainingprocessofBPNNinvolvesfeedingtheinputdataforwardthroughthenetwork,calculatingtheerrorbetweenthepredictedoutputandt