Research on Circuit Breaker Fault Diagnosis Method Based on Principal Component Analysis-support Vector Machine Optimization Model

FAN Hao; LI Xingwen; SU Haibo; CHEN Li; SHI Zongqian

doi:10.13296/j.1001-1609.hva.2020.06.021

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Research on Circuit Breaker Fault Diagnosis Method Based on Principal Component Analysis-support Vector Machine Optimization Model

更新时间：2025-04-26

- Research on Circuit Breaker Fault Diagnosis Method Based on Principal Component Analysis-support Vector Machine Optimization Model
- High VoltageApparatus Vol. 56, Issue 6, Pages: 143-151(2020)
- 作者机构：
  
  1. 西安交通大学电气工程学院
  2. 广州供电局有限公司电力试验研究院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2020.06.021
  CLC： TM561
- Published：2020
- 稿件说明：
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[1]樊浩,李兴文,苏海博,陈立,史宗谦.基于主成分分析——支持向量机优化模型的断路器故障诊断方法研究[J].高压电器,2020,56(06):143-151.

FAN Hao, LI Xingwen, SU Haibo, et al. Research on Circuit Breaker Fault Diagnosis Method Based on Principal Component Analysis-support Vector Machine Optimization Model[J]. High voltageapparatus, 2020, 56(6): 143-151.
[1]樊浩,李兴文,苏海博,陈立,史宗谦.基于主成分分析——支持向量机优化模型的断路器故障诊断方法研究[J].高压电器,2020,56(06):143-151. DOI： 10.13296/j.1001-1609.hva.2020.06.021.

FAN Hao, LI Xingwen, SU Haibo, et al. Research on Circuit Breaker Fault Diagnosis Method Based on Principal Component Analysis-support Vector Machine Optimization Model[J]. High voltageapparatus, 2020, 56(6): 143-151. DOI： 10.13296/j.1001-1609.hva.2020.06.021.

摘要

为了准确实现电磁斥力机构真空断路器的故障诊断

针对电磁斥力机构真空断路器分闸振动信号

在正常状态、单一故障状态、多个故障共存状态下

通过小波包分解、希尔伯特-黄变换（HHT）提取振动信号能量熵向量

结合特征提取方法原理分析了两种方法提取特征量的有效性

提出了主成分分析（PCA）—支持向量机（SVM）优化模型进行故障诊断

并验证了网格搜索算法（GSA）、粒子群算法（PSO）、遗传算法（GA）3种参数寻优算法的性能。经实验测试

PCA-SVM优化模型解决了因样本特征信息存在噪声和冗余而引起的SVM识别准确率下降的问题

有效提升了测试样本的识别准确率和模型效率

具有较好的诊断效果。

Abstract

In order to accurately realize the fault diagnosis of electromagnetic repulsive mechanism vacuum circuitbreaker

according to the breaking vibration signal of the electromagnetic repulsion mechanism vacuum circuitbreaker

the energy entropy vector of the vibration signal is extracted by wavelet packet decomposition and Hilbert-Huang transform（HHT）in the normal state

single fault state and multiple fault coexistence states

combining theprinciple of feature extraction method

the effectiveness of the two methods for extracting feature quantities is ana-lyzed. A principal component analysis（PCA）-support vector machine（SVM）optimization model is proposed forfault diagnosis. The performance of three parameter optimization algorithms

namely grid search algorithm（GSA）

particle swarm optimization（PSO）and genetic algorithm（GA）

is verified. Through experimental tests

the PCA-SVM optimization model solves the problem of SVM recognition accuracy caused by noise and redundancy of samplefeature information

which effectively improves the recognition accuracy and model efficiency of test samples

andhas a good diagnostic effect.

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