一種直流配電網(wǎng)電能質(zhì)量擾動識別方法

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引用格式：，，.一種直流配電網(wǎng)電能質(zhì)量擾動識別方法[J].現(xiàn)代電子技術(shù)，2025，48（10）：118-126

關(guān)鍵詞：直流配電網(wǎng)；電能質(zhì)量；擾動識別；DBSCAN聚類；功率譜密度；核主成分分析；麻雀搜索算法；支持向量機(jī)中圖分類號：TN86-34；TM711 文獻(xiàn)標(biāo)識碼：A 文章編號：1004-373X（2025）10-0118-09

Abstract：With theincreaseof powerelectronicdevicesconnectedtothepowergrid，direct-current （DC）distribution networksaresuperiortotraditionaldistributionnetworksintermsofstrongtransmissonperformance，reducedlinelosses，and newenergyconsumption，graduallybecominganew trendin futuredistributiondevelopment.Toensurethestableoperationof the DC distributionnetworkandensure powerqualityanimproved sparowsearch algorithm-support vector machine （ISSA-SVM） powerqualitydisturbanceidentificationmethodbasedonkernelprincipalcomponentanalysis（KPCA）featuredimensionality reduction is proposed.Theformationmechanismsofvariouspowerqualityisueswerethoroughlyexplored，andsixfeatureswere extractedbasedonwaveformanalysis.TheDBSCANclusteringmethodisusedtodetectthepresenceofoutliersanddetermine whethertouseKPCAtoreducethedimensionalityoffeatures，enabling themtoachievegoodclusteringindiferentdata situations;The ISSAisusedtooptimizetheparametersofSVM，andtheSVMmodelisretrainedusingtheoptimizationresults. Theexperimentalresultsshowthattheproposedmethod has highaccuracyandcanefectivelyidentifypowerqualitydisturbance signals.

Keywords：DC distributiongrid;powerquality；disturance identification;DBSCANclustering;powerspectral density kernel principal component analysis;sparrow search algorithm;support vector machine

0 引言

隨著新型電力系統(tǒng)的建設(shè)，相比于交流配電網(wǎng)而言，直流配電網(wǎng)具有靈活易控、傳輸損耗小，可提升電能質(zhì)量、可良好地接受各種新型直流能源以及減少成本等優(yōu)點(diǎn)[。（剩余9432字）