Fault Diagnosis of High Voltage Disconntector Mechanism Based on Stator Current Characteristics and SVM

LIU Shibing; SONG Lingcan; GUO Wenjing; LIU Wei

doi:10.13296/j.1001-1609.hva.2020.06.042

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Fault Diagnosis of High Voltage Disconntector Mechanism Based on Stator Current Characteristics and SVM

更新时间：2025-04-26

- Fault Diagnosis of High Voltage Disconntector Mechanism Based on Stator Current Characteristics and SVM
- High VoltageApparatus Vol. 56, Issue 6, Pages: 289-295(2020)
- 作者机构：
  
  华东交通大学电气与自动化工程学院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2020.06.042
  CLC： TM564.1
- Published：2020
- 稿件说明：
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[1]刘仕兵,宋陵灿,郭文璟,刘威.基于定子电流特征与SVM高压隔离开关机构故障诊断[J].高压电器,2020,56(06):289-295.

LIU Shibing, SONG Lingcan, GUO Wenjing, et al. Fault Diagnosis of High Voltage Disconntector Mechanism Based on Stator Current Characteristics and SVM[J]. High voltageapparatus, 2020, 56(6): 289-295.
[1]刘仕兵,宋陵灿,郭文璟,刘威.基于定子电流特征与SVM高压隔离开关机构故障诊断[J].高压电器,2020,56(06):289-295. DOI： 10.13296/j.1001-1609.hva.2020.06.042.

LIU Shibing, SONG Lingcan, GUO Wenjing, et al. Fault Diagnosis of High Voltage Disconntector Mechanism Based on Stator Current Characteristics and SVM[J]. High voltageapparatus, 2020, 56(6): 289-295. DOI： 10.13296/j.1001-1609.hva.2020.06.042.

摘要

针对牵引供电系统中高压隔离开关机构故障诊断问题

提出了一种基于定子电流特征与支持向量机（SVM）的故障诊断方法。通过分析转矩波动与定子电流之间的关系

建立了定子电流波动与边频带和基波有效值之间的函数关系

得出定子电流的边频分量和基波有效值可有效地表现出机构故障类型。然后设计了低通Butterworth滤波器+数字FIR滤波器的采样滤波组合

对采样定子电流数据分析处理得出45～55 Hz边频量的幅值—频率特征量和分合闸过程基波有效值—时间特征量

以这两种电流特征量作为SVM的输入

构建SVM故障诊断模型。最后实验结果表明该模型对高压隔离开关机构故障类别诊断的总体准确度可达到97.8%。

Abstract

Aiming at the problem of fault detection of high voltage disconntector mechanism in traction power supplysystem

a fault diagnosis method based on stator current characteristics and SVM is proposed. By analyzing the rela-tionship between the torque fluctuation and the stator current

the functional relationship between the stator currentfluctuation and the effective value of the side frequency band and the fundamental wave is established

and it is con-cluded that the side frequency component and the effective value of the fundamental wave of the stator current can ef-fectively show the type of mechanism fault. Then

the combination of low-pass Butterworth filter and digital FIR filteris designed. The amplitude frequency characteristic value of the 45 ～ 55 Hz side frequency and the fundamental ef-fective value time characteristic value of the opening and closing process are obtained by analyzing and processingthe sampled stator current data. These two current characteristics are used as the input of SVM to build the SVM faultdiagnosis model. Finally

the experimental results show that the overall accuracy of the model is 97.8%.

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