微型电脑应用2024,Vol.40Issue(7) :222-225.

基于图卷积神经网络的输电线路故障缺陷状态诊断

State Diagnosis of Transmission Line Faults and Defects Based on Graph Convolutional Neural Network

孟亚宏 余翰知
微型电脑应用2024,Vol.40Issue(7) :222-225.

基于图卷积神经网络的输电线路故障缺陷状态诊断

State Diagnosis of Transmission Line Faults and Defects Based on Graph Convolutional Neural Network

孟亚宏 1余翰知1
扫码查看

作者信息

  • 1. 国网江苏省电力有限公司滨海县供电分公司,江苏,盐城 224500
  • 折叠

摘要

为了解决输电线路故障发生较多、告警系统误报率较高且依赖运维人员分析等问题,提出基于图卷积神经网络的输电线路故障缺陷诊断方法.根据历史输电线路缺陷数据评价得到输电线路缺陷状态;利用k-means算法进行数据离散化处理,提取输电线路缺陷特征,构建特征向量;使用马氏距离来表示各个向量之间的相似度,构建图结构;利用图卷积神经网络实现输电线路故障缺陷类别分类,准确识别输电线路故障缺陷状态.实验结果表明,本文提出的方法做出的诊断结果更加精确,综合诊断效果最好.

Abstract

In order to solve the problems of frequent transmission line faults,high false alarm rate of alarm system,and depend-ence on operation and maintenance personnel analysis,this paper proposes a fault defect diagnosis method of transmission line based on graph convolutional neural network.According to the evaluation of the historical transmission line defect data,the transmission line defect state is obtained,and the k-means algorithm is used for data discretization to extract the transmission line defect features and construct feature vectors.The Mahalanobis distance is used to represent the similarity between each vector and construct the graph structure.The graph convolutional neural network is used to realize the classification of trans-mission line faults and defects,and accurately identify the state of transmission lines faults and defects.The experiment results show that the proposed method makes more accurate diagnosis results and has the best comprehensive diagnosis effect.

关键词

图卷积神经网络/输电线路/故障诊断/图结构

Key words

graph convolutional neural network/transmission line/fault diagnosis/graph structure

引用本文复制引用

出版年

2024
微型电脑应用
上海市微型电脑应用学会

微型电脑应用

CSTPCD
影响因子:0.359
ISSN:1007-757X
段落导航相关论文