Mechanical Fault Diagnosis Method of High-voltage Disconnector Based on Empirical Modal Decomposition and Support Vector Machine

GUO Yujing; CHEN Shigang; LI Shaohua; LI Hongtao; JIN Guangyao; ZHANG Wentao; ZHANG Yiming; GUAN Yonggang

doi:10.13296/j.1001-1609.hva.2018.09.003

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Mechanical Fault Diagnosis Method of High-voltage Disconnector Based on Empirical Modal Decomposition and Support Vector Machine

更新时间：2025-04-26

- Mechanical Fault Diagnosis Method of High-voltage Disconnector Based on Empirical Modal Decomposition and Support Vector Machine
- High VoltageApparatus Vol. 54, Issue 9, Pages: 12-18(2018)
- 作者机构：
  
  1. 平高集团有限公司
  2. 北京交通大学电气工程学院
  3. 国网江苏省电力公司电力科学研究院
  4. 清华大学电机系电力系统及发电设备控制和仿真国家重点实验室
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2018.09.003
  CLC： TM564.1
- Published：2018
- 稿件说明：
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[1]郭煜敬,陈士刚,李少华,李洪涛,金光耀,张文涛,张一茗,关永刚.基于经验模态分解及支持向量机的高压隔离开关机械故障诊断方法[J].高压电器,2018,54(09):12-18.

GUO Yujing, CHEN Shigang, LI Shaohua, et al. Mechanical Fault Diagnosis Method of High-voltage Disconnector Based on Empirical Modal Decomposition and Support Vector Machine[J]. High voltageapparatus, 2018, 54(9): 12-18.
[1]郭煜敬,陈士刚,李少华,李洪涛,金光耀,张文涛,张一茗,关永刚.基于经验模态分解及支持向量机的高压隔离开关机械故障诊断方法[J].高压电器,2018,54(09):12-18. DOI： 10.13296/j.1001-1609.hva.2018.09.003.

GUO Yujing, CHEN Shigang, LI Shaohua, et al. Mechanical Fault Diagnosis Method of High-voltage Disconnector Based on Empirical Modal Decomposition and Support Vector Machine[J]. High voltageapparatus, 2018, 54(9): 12-18. DOI： 10.13296/j.1001-1609.hva.2018.09.003.

摘要

文中将K-means聚类算法和经验模态分解（empirical mode decomposition

EMD）相结合

对隔离开关机械故障进行诊断。为验证方法的有效性

搭建隔离开关运行状态在线监测系统

在某252 k V隔离开关的操动机构上选定位置安装了传感器

采集了机械振动等信号在模拟故障下的大量数据。首先利用小波包降噪方法对信号进行预处理;其次

应用EMD和谱分析方法对振动信号进行经验模态分解

得到IMF分量并将其能量熵作为特征量;然后

通过K-means聚类算法验证了特征提取方式的有效性;最后

通过支持向量机算法（support vector machine

SVM）对样本进行训练

实现了机械故障的准确诊断

验证了该方法的有效性。

Abstract

K-means clustering algorithm and EMD（empirical mode decomposition）are employed to diagnose me-chanical fault of isolating switch. To verify the validity of the method

an on-line monitoring system of running stateof disconnector is set up. A sensor is installed on the selected position of the actuating mechanism of a 252kV isolat-ing switch

and a large number of mechanical vibration data of the isolating switch with analog fault are collected.The signals are preprocessed firstly by wavelet packet noise reduction method

then EMD and spectral analysis meth-od are applied for the mode decomposition of the vibration signals to obtain the IMF component

and the energy entro-py of the IMF component is taken as the characteristic quantity. The effectiveness of the feature extraction method isverified by K-means clustering algorithm. Moreover

support vector machine（SVM）is used to train the samples

hence accurate diagnosis of the mechanical faults is realized

and the validity of the proposed method is verified.

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