电测与仪表2026，Vol.63Issue(3)：78-85,8.DOI:10.19753/j.issn1001-1390.2026.03.008
一维CNN多模型融合电能质量扰动分类方法
Power quality disturbance classification method based on fusion of multiple 1-D CNN model
摘要
Abstract
Aiming at the problem of power quality disturbance(PQD)classification,classification sub-models based on one-dimension CNN in the time domain(original signal),frequency domain(Fourier transform)and time-frequency domain(wavelet transform)are modified.And then,a fusion model of classification result based onback propagation(BP)neural network is constructed to realize PQD classification.By simulation experiment and comparative analysis,the fusion method is proved to have high accuracy and good robustness under the premise of low computational complexity.The classification accuracy is higher than 99.7%with SNR not less than 30 dB,while the accuracy of 99.58%,99.33%and 98.91%can still be maintained at 20 dB,15 dB and 10 dB SNR.The method is proved valuable in practical application,and provides a reference for the further study of fusion method-based PQD classification.关键词
电能质量扰动/分类识别/一维卷积神经网络/BP神经网络/时频域Key words
power quality disturbance/classification and recognition/1-D convolutional neural network/BP neural network/time-frequency domain分类
信息技术与安全科学引用本文复制引用
陆春光,宋磊..一维CNN多模型融合电能质量扰动分类方法[J].电测与仪表,2026,63(3):78-85,8.基金项目
国网浙江省电力有限公司科技项目(5211YF220008) （5211YF220008）
