高压电器2024，Vol.60Issue(7)：210-220,11.DOI:10.13296/j.1001-1609.hva.2024.07.023
电力电缆局放在线监测神经网络自动识别精度的提升方法
Improvement Method of Automatic Identification Accuracy of On-line Monitoring Neural Network for Partial Discharge of Power Cable
摘要
Abstract
For minimizing the possibility of misjudgment and false alarm of partial discharge(PD)signal identifica-tion in the operation of PD online monitoring system of cable as much as possible,PD's interpretation mechanism and identification accuracy of partial discharge are improved and updated.The warning record data of tens of thou-sands suspected PD in cables collected by the field operation system is analyzed and classified,filtering process of in-ter-phase signal correlation is added to the automatic logic discrimination program of PD,the existed neural network is optimized in structure and the pre-processing of the learning data of the existing neural network is improved.Three kinds of partial discharge data,including field recording,analog signal generator,and artificial partial discharge mod-el with voltage application experiment,are used to test the improved PD discrimination program.The results show that the enhanced PD discrimination program can not only reduce the warning rate of non-PD warning data to the ex-pected level of 5%,but also,at the same time,improve the accuracy of identifying various types of PD signals generat-ed at artificial simulation,reducing significantly the possibility of misjudgment and false reporting in the PD online monitoring.关键词
电缆/局部放电/在线监测/相间关系/神经网络/学习数据/前置处理/识别精度Key words
cable/partial discharge/online monitoring/inter-phase signal correlation/neural network/learning data/pre-processing/identification accuracy引用本文复制引用
孙廷玺,方义治,郑晓东,雷小月,姜志彬,周智鹏,陈敏..电力电缆局放在线监测神经网络自动识别精度的提升方法[J].高压电器,2024,60(7):210-220,11.基金项目
广东电网有限责任公司科技项目资助(GDKJXM20185374). Project Supported by Science and Technology Project of Guangdong Power Grid Co.(GDKJXM20185374). （GDKJXM20185374）
