适用于深度学习训练的配电网故障历史样本数据生成研究

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中图分类号：TP18 文献标志码：A 文章编号：2095-2945(2025)14-0063-0

Abstract:Withthedevelopmentofsmartgridanddeep learning technology，theuseof historicalfaultsamples fortraining hasbecomeapowerful meansoffaultdataprocessingindistributionnetworks.Thisstudyadoptsadynamiciterativestrategy: firstly,thedeeplearnigmodelisusedtoidentifythefaulttypesofthedistributionnetwork,andthekeydataissummarized andextractedfromtheidentificationprocessThen，throughacontinuousiterativeprocess，thehistoricalsampledatageerated each time is fed back into the model. Finally,a system model of 10kV line protection detection test is built in Matlab/Simulink, andasimulationtestexampleisbuiltforverification,andtheexperimentalresultsshowthatthemodelisefectiveandfeasible.

Keywords:distributionnetwork failures;faultdetection;deep leaming;dynamic iteration;historical sampledata

在当今信息化、智能化的时代背景下，配电网的自动化水平和智能化[1-2程度不断提高，对故障诊断技术提出了更高的要求。（剩余8178字）