电测与仪表2024，Vol.61Issue(10)：97-103,7.DOI:10.19753/j.issn1001-1390.2024.10.013
基于CEEMDAN和卷积神经网络的配电网故障选线新方法
A novel fault line selection method for distribution network based on CEEMDAN and convolutional neural network
摘要
Abstract
A fault line selection method for distribution network based on complete ensemble empirical mode de-composition with adaptive noise(CEEMDAN)and convolutional neural network(CNN)is proposed.Firstly,the CEEMDAN algorithm is used to decompose zero-sequence current signal of each line to obtain the intrinsic function of zero-sequence current of each line.Secondly,the intrinsic functions of each line are spliced together in order to obtain a time-frequency data matrix containing abandunt fault features,which corresponds to the current system op-erating conditions.Finally,the fault eigenvector of the time-frequency data matrix are independently mined by CNN,and the fault line selection of distribution network is realized through the fault line number output of softmax function.Simulation experiments show that the method is independent of transition resistance,detection time delay and other factors,which can accurately and effectively identify the fault lines.关键词
故障选线/CEEMDAN/卷积神经网络/数据矩阵/故障特征向量Key words
fault line selection/CEEMDAN/convolutional neural network/data matrix/fault eigenvector分类
信息技术与安全科学引用本文复制引用
马红月,李温静,吴文炤,张楠,王婧..基于CEEMDAN和卷积神经网络的配电网故障选线新方法[J].电测与仪表,2024,61(10):97-103,7.基金项目
国家重点研发计划资助项目(2020YFB0905900) （2020YFB0905900）
