基于EEMD融合BAS-CNN的高压电缆故障诊断  

High-voltage cable fault diagnosis based on EEMD and BAS-CNN

作　　者：肖旰 周莉[1] 李敬兆[1] 刘泽朝 张珂 Xiao Gan;Zhou Li;Li Jingzhao;Liu Zechao;Zhang Ke(School of Electrical and Information Engineering,Anhui University of Science and Technology,Huainan 232000,China)

机构地区：[1]安徽理工大学电气与信息工程学院,淮南232000

出　　处：《电子测量技术》2022年第4期160-167,共8页Electronic Measurement Technology

基　　金：国家自然科学基金(51874010,61170060);北京理工大学高精尖机器人开放性研究项目(2018IRS16);物联网关键技术研究创新团队(201950ZX003)资助。

摘　　要：针对高压电缆故障复杂程度高,实时监测成本过高的问题,提出一种基于集合经验模态分解(EEMD)和天牛须搜索算法优化卷积神经网络(CNN)的组合诊断方法。将电缆护层电流数据经EEMD分解为若干个本征模态分量(IMF),结合相关系数选取与原信号相关度最大的分量,作为CNN网络的输入。为了提高网络模型的分类精度,使用天牛须算法(BAS)优化CNN诊断模型的超参数。以淮南某煤矿高压电缆电流数据为例,实验结果表明,EEMD有效的将电流信号进行分解,所设计的BAS-CNN网络与2组人为确定CNN超参数的网络对比,BAS-CNN具有最高的分类精度,监测准确率达到96.95%。For the problem of high complexity of high-voltage cable faults and high cost of real-time monitoring,a combined diagnosis method based on ensemble empirical modal decomposition(EEMD)and tennies whisker search algorithm optimized convolutional neural network(CNN)is proposed.The cable sheath current data are decomposed into several eigenmodal components(IMF)by EEMD,and the component with the highest correlation with the original signal is selected by combining the correlation coefficients as the input of the CNN network.In order to improve the classification accuracy of the network model,the hyperparameters of the CNN diagnostic model are optimized using the aspen whisker algorithm(BAS).Taking the high-voltage cable current data of a coal mine in Huainan as an example,the experimental results show that EEMD effectively decomposes the current signal,and the designed BAS-CNN network has the highest classification accuracy with 96.95%monitoring accuracy compared with 2groups of networks with artificially determined CNN hyperparameters.

关 键 词：护层电流 EEMD BAS-CNN 故障检测 

分 类 号：TM73[电气工程—电力系统及自动化]