| 王 毅,李 曙,李松浓,李 杰,杨芾藜,郑 可.瞬时特征下极限学习机在接地故障诊断中的应用[J].电子测量与仪器学报,2022,36(1):212-219 |
| 瞬时特征下极限学习机在接地故障诊断中的应用 |
| Application of ground fault diagnosis based on extreme learningmachine under instantaneous characteristics |
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| DOI： |
| 中文关键词: 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 |
| 英文关键词:single-phase ground fault fault type identification fault line selection Hilbert-Huang transform extreme learning machine grey wolf optimizer |
| 基金项目:重庆市自然科学基金 (cstc2016jcyjA0214)项目资助 |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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%. |
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