Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on S-transform and Extreme Learning Machine

HUANG Nantian; CHEN Huaijin; LIN Lin; QI Jiajin

doi:10.13296/j.1001-1609.hva.2018.06.011

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Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on S-transform and Extreme Learning Machine

更新时间：2025-04-26

- Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on S-transform and Extreme Learning Machine
- High VoltageApparatus Vol. 54, Issue 6, Pages: 74-80(2018)
- 作者机构：
  
  1. 东北电力大学电气工程学院
  2. 国网济宁供电公司
  3. 吉林化工学院信息与控制工程学院
  4. 国网杭州供电公司
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2018.06.011
  CLC： TM561
- Published：2018
- 稿件说明：
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[1]黄南天,陈怀金,林琳,戚佳金.基于S变换和极限学习机的高压断路器机械故障诊断[J].高压电器,2018,54(06):74-80.

HUANG Nantian, CHEN Huaijin, LIN Lin, et al. Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on S-transform and Extreme Learning Machine[J]. High voltageapparatus, 2018, 54(6): 74-80.
[1]黄南天,陈怀金,林琳,戚佳金.基于S变换和极限学习机的高压断路器机械故障诊断[J].高压电器,2018,54(06):74-80. DOI： 10.13296/j.1001-1609.hva.2018.06.011.

HUANG Nantian, CHEN Huaijin, LIN Lin, et al. Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on S-transform and Extreme Learning Machine[J]. High voltageapparatus, 2018, 54(6): 74-80. DOI： 10.13296/j.1001-1609.hva.2018.06.011.

摘要

机械故障是高压断路器最常见的故障

研究高压断路器机械故障诊断方法对于提高电力系统可靠性具有重要意义。为提高高压断路器机械故障诊断的效率

文中提出一种基于S变换和极限学习机（ELM）的高压断路器机械故障诊断新方法。首先

对高压断路器动作期间产生的振动信号进行S变换处理

获得相应的时—频矩阵;然后

对S变换模值矩阵进行时域和频域划分

计算振动信号在不同时段和频段的局部奇异值

并选择各子矩阵的最大奇异值作为故障诊断的特征向量;最后

采用ELM对高压断路器机械状态进行分类。对高压断路器在正常和故障状态下进行诊断实例测试

实验结果表明

该方法能够快速准确地识别断路器机械状态

具有较高的诊断效率。

Abstract

To improve the fault diagnosis efficiency of high voltage circuit breakers（HVCBs）

a new mechanical fault diagnosis method for HVCBs based on S-transform and extreme learning machine（ELM） is proposed.Firstly

the vibration signal generated during the operation of HVCBs is processed by S-transform to get the corresponding time-frequency matrix. Then

the S-transform module matrix is divided into several submatrices in time domain and frequency domain to compute the local singular values of vibration signal in different time and frequency sections. And the largest singular value of each submatrix is selected to form the feature vector for fault diagnosis. Finally

ELM algorithm is used for classification of the high voltage circuit breaker’s mechanical state. In addition

diagnostic experiments are conducted on a real HVCB in respective normal and fault state

and the results show that the proposed method can quickly and accurately recognize mechanical status of HVCBs with high efficiency.

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