摘要
为消除小电流系统下接地故障诊断准确性受系统中性点接地方式、故障类型以及故障位置等因素的影响,分析了系统在各类单相接地故障下的零序电流,提出了一种基于改进的希尔伯特-黄变换和极限学习机的接地故障检测方法。首先用小波变换对信号进行多频带划分,再根据对地电容的充放电特性筛选出的特征频带并进行希尔伯特-黄变换,得到各条线路零序电流的瞬时能量特征,最后利用灰狼算法和粒子群算法对极限学习机进行多层次优化,得到同时具有故障类型识别和选线功能的分类器。设计一款基于数字故障指示器采集和主站数据处理的故障检测系统。经测试,该方法能准确判断故障类型并完成选线,准确度达到90%以上。
In order to eliminate the influence of grounding mode,fault type and fault location on the accuracy of ground fault diagnosis in low current system.By analyzing the zero sequence current of all kinds of single-phase ground faults in this system,a single-phase ground fault detection method was proposed on the basis of the improved Hilbert-Huang transform(HHT)and Extreme learning machine(ELM).This method firstly used wavelet transform(WT)for multiband signal.Then HHT was performed on the characteristic signal that was selected by the charging and discharging characteristics of the ground capacitance to obtain the instantaneous energy of the zero sequence current of each line.Finally,gray wolf optimization(GWO)and particle swarm optimization(PSO)were used to optimize the ELM model to obtain the GWO-PSO-ELM model with fault type recognition and line selection functions.A fault detection system based on digital fault indicator(DFI)acquisition platform and master station data processor is designed.The test results show that this method can accurately judge the fault type and complete line selection,and the accuracy reaches more than 90%.
作者
王毅
李曙
李松浓
李杰
杨芾藜
郑可
Wang Yi;Li Shu;Li Songnong;Li Jie;Yang Fuli;Zheng Ke(Communication and Information Engineering College,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Chongqing Electric Power Research Institute,Chongqing 400014,China;Postdoctoral Workstation of the Chongqing Electric Power Corporation,Chongqing 400014,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2022年第1期212-219,共8页
Journal of Electronic Measurement and Instrumentation
基金
重庆市自然科学基金(cstc2016jcyjA0214)项目资助
关键词
单相接地故障
故障类型识别
故障选线
希尔伯特黄变换
极限学习机
灰狼算法
single-phase ground fault
fault type identification
fault line selection
Hilbert-Huang transform
extreme learning machine
grey wolf optimizer
作者简介
王毅,2004年于电子科技大学获得学士学位,2009年于北京邮电大学获得博士学位,现为重庆邮电大学副教授,主要研究方向为智能电网、电力物联网。E-mail:wangyi81@cqupt.edu.cn;李曙,2018年于湖北经济学院获得学士学位,现为重庆邮电大学硕士研究生,主要研究方向为配电故障监测与诊断。E-mail:leeshu077@163.com
