电力信息与通信技术2024，Vol.22Issue(4)：11-20,10.DOI:10.16543/j.2095-641x.electric.power.ict.2024.04.02
基于图表示学习与知识蒸馏的电缆故障快速识别方法
A Fast Cable Fault Identification Method Based on Graph Representation Learning and Knowledge Distillation
摘要
Abstract
In the early warning of equipment failures in traction power supply systems,accurate and rapid identification of early cable failures is a key technology for intelligent operation and maintenance.In order to mine the deep information of feature construction and solve the problem of engineering deployment iteration rate,this paper proposes a cable fault identification method based on graph representation learning and knowledge distillation.First,the current signal of the cable is sampled and analyzed,and the feature information under the time series is dynamically displayed and updated with graph features.The convolutional autoencoder is used to reconstruct the feature image with noise reduction,and then the graph convolution neural network based on knowledge distillation is used.The network identification algorithm builds a teacher-student network fault identification model.The study builds a cable fault model in the PSCAD simulation environment to collect overcurrent disturbance signals,and proves the effectiveness and accuracy of the model through experimental comparisons,and greatly improves the model iteration rate,and at the same time enhances the robustness under noise disturbances,and has engineering application value.关键词
电缆早期故障/卷积自编码器/图表示学习/知识蒸馏Key words
cable early fault/convolutional auto-encoder/graph representation learning/knowledge distillation分类
信息技术与安全科学引用本文复制引用
余盛灿,余涛,陈鑫沛,杨家俊,潘振宁..基于图表示学习与知识蒸馏的电缆故障快速识别方法[J].电力信息与通信技术,2024,22(4):11-20,10.基金项目
国家自然科学基金项目(52207105). （52207105）
