电力信息与通信技术2026，Vol.24Issue(6)：53-61,9.DOI:10.16543/j.2095-641x.electric.power.ict.2026.06.06
基于电力线通信的电力线智能诊断(三):人工智能辅助的监测与定位
Intelligent Cable Diagnosis Based on Power Line Communication Ⅲ:Artificial Intelligence-assisted Monitoring and Positioning
摘要
Abstract
Power line communication and power frequency energy transmission share the same medium,and the subtle dynamic changes in the physical properties of power lines will significantly affect the transmission characteristics of broadband communication signals.The technology of using these changes to detect the status of power lines in reverse still lacks a systematic solution.In response to this issue,this paper proposes an intelligent monitoring method for power line anomalies based on power line communication,which combines power line communication with artificial intelligence to achieve real-time monitoring and recognition of various types of power line anomalies in complex and dynamically changing network environments.Firstly,the power line channel containing abnormal features is modelled,and the channel frequency response at the receiving end through pilot signals is estimated.The random forest algorithm is used to complete online monitoring and identification of power line aging,and predict the degree of aging.The estimated impedance spectrum is used,and a generalized integral transformation on it is performed to obtain the ranging function,and the localization of abnormal power line segments is achieved.An experimental platform built to test aging power lines has verified the effectiveness and practicality of this method,indicating that it can serve as an effective solution for monitoring,identifying,warning,and locating power line aging,and has important practical application value.关键词
电力线通信/信道频率响应/机器学习/异常定位Key words
power line communication/channel frequency response/machine learning/fault segment localization分类
信息技术与安全科学引用本文复制引用
梁栋,雒美娟,刘虎,王梓伦,张怡华,赵腾卓,何雨阳,宋凡,胡正伟..基于电力线通信的电力线智能诊断(三):人工智能辅助的监测与定位[J].电力信息与通信技术,2026,24(6):53-61,9.基金项目
国家自然科学基金项目(52207174/52177083) （52207174/52177083）
陕西省自然科学基金项目(2023-JC-YB-398). （2023-JC-YB-398）
