基于GRNN的特高壓直流輸電線路故障識別方法

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關鍵詞：廣義回歸神經(jīng)網(wǎng)絡；特高壓直流輸電線路；繼電保護；廣義S變換；故障識別DOI：10.15938/j. jhust.2025.02.014中圖分類號：TM723 文獻標志碼：A 文章編號：1007-2683（2025）02-0131-09

Abstract：Aprotection methodforultra-highvoltage directcurenttransmisson linesbasedongeneralizedregressonneural network（GRNN）isproposedtoaddresstheisuesofeasyrejectionandlongfault detectiontimeinultra-highvoltagedirectcurrent protection.FirstlyasedonthegneralizedStransfo，thefaultcharacterisiciformationintefrequencydomainistaindto constructhe input data for GRNN.Secondly，thechaosquantum particleswarm optimization（CQPSO）algorithm isused tooptimize theparametersofthegeneralizedregressonneuralnetwork，foranidealnetworkmodelbasedontheprincipleofthelowestfinss function，andbeterlathultcharacterissofthulra-hghvageDCtrasmssonlne.TeSofaxlasifierisuiledto clasifydep-levelfeatures，dentifingfaultsasexteal，us，orlinefults，andpolarizingtemintopositive，negative，orbipolar faults，thenoutpuigrecogitioesultsinally，heultra-higagediecturnt tasmissnodelbuiltineCA/C simulatioenvronmentisvalidated，andtevadationresultsshowedthattheproposedmethodhasgoodpeformanceinfaultetection andfaultpoleselectionofultra-highvoltagedirectcurenttransmissionlinerelaprotectionComparedtotradionalonolutional neuralnetwoks，generalizedregressionneuralnetworks，upportvectormacines，andotermethods，thefultrecognitionacyof the proposed method in this paper has been improved by 6.6% ， 0.65% ，and 7.69% ，respectively，meeting the requirements of protection speed and reliability.

Keywords：generalizedregressonneuralnetwork；UHVDCtransmissionline；relayprotection；generalizedS-transform；faulti dentification

0 引言

隨著新型電力系統(tǒng)建設的推進，特高壓直流輸電由于其傳輸容量大、傳輸距離遠、線路損耗少等顯著優(yōu)勢，可以很好地接入分布式電源和儲能裝置，進而推進我國“雙碳”目標，故研究特高壓直流輸電技術已經(jīng)成為熱點。（剩余13451字）