基于卷積神經(jīng)網(wǎng)絡(luò)和Transformer的電能質(zhì)量擾動分類

打開文本圖片集

中圖分類號：TN915.04-34；TP183；TM711 文獻標識碼：A 文章編號：1004-373X（2025）16-0113-10

PQDsclassificationbasedonconvolutional neural networkandTransformer

WANGGaofeng，ZHANGHao，QIANYun，GAOMan

Abstract：With the large-scaleapplication of new energy，the probabilityof power quality disturbance （PQDs）events is increasedsignificantlyandthesedisturbancescancausesignificantloses topowerdistributionsystems.Therefore，amethodof PQDclassification basedonconvolutional neuralnetwork（CNN）and Transformerisproposed，namely CTranCAM.Inthis method，theconvolutionoperationofCNNisusedtoautomaticallyextractlocalfeaturesofPQDssignal timeseries，andthen multi-headatentionmechanisminTransformerisusedtomodeltheglobalandlong-termrelationshipsofteextractedfeatures tocompensatefortheshortcomingsofCNNinprocesingglobalinformation.Therecognitionresultsareoutputbymeansoffully conectedlayer.Thesimulationof25typesofsynthesizedPQDsdataisconductedbymeansof CTranCAMmethod.Theresults showthat the classification accuracy of this method is99.60% under noise free conditions，and canreach 99.20%， 99.36% ，and 99.40% at signal-to-noiseratiosof 3OdB，40dB，and5OdB，respectively.Ithasgoodnoiseresistanceand generalization performace.Incomparison withperformanceofothermethods，theproposed methodhasgood classfication performancein different noise environments，which is an excellent PQDs classification method.

Keywords：powerquality disturbance;convolutional neural network；Transformer model; multi-headatention mechanism; featureextraction;classificationperformance

0 引言

隨著資源枯竭和環(huán)境惡化問題日益嚴峻，可再生能源如太陽能和風能的廣泛應(yīng)用成為主要解決方案。（剩余11535字）