Research on Fault Diagnosis of Traction Power Supply System Based on PSO-LSSVM

Lei ZHANG

doi:10.13890/j.issn.1000-128x.2019.03.011

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Research on Fault Diagnosis of Traction Power Supply System Based on PSO-LSSVM

更新时间：2021-12-13

- Research on Fault Diagnosis of Traction Power Supply System Based on PSO-LSSVM
- Electric Drive for Locomotives Issue 3, Pages: 51-55,59(2019)
- 作者机构：
  
  1.广东科贸职业学院　信息与自动化学院，广东　广州　510430
- 作者简介：
- 基金信息：
- DOI：10.13890/j.issn.1000-128x.2019.03.011
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Lei ZHANG. Research on Fault Diagnosis of Traction Power Supply System Based on PSO-LSSVM. [J]. Electric Drive for Locomotives (3):51-55,59(2019)
DOI：

Lei ZHANG. Research on Fault Diagnosis of Traction Power Supply System Based on PSO-LSSVM. [J]. Electric Drive for Locomotives (3):51-55,59(2019) DOI： 10.13890/j.issn.1000-128x.2019.03.011.

摘要

为了提高列车运行稳定性，针对牵引供电系统故障诊断进行研究。根据牵引供电系统工作原理和特性分析故障现象与发生原因，提取用于故障诊断的特征信号；建立基于粒子群优化算法（Particle Swarm Optimization，简称PSO）优化最小二乘支持向量机（Least Squares Support Vector Machine，简称LSSVM）的故障诊断模型，并使用主成分分析（Principal Component Analysis，简称PCA）算法提取数据特征作为故障诊断模型的输入来降低输入维数；使用多种故障诊断模型进行对比分析。研究结果表明：经过PCA算法提取特征的PSO-LSSVM故障诊断模型具有较高的识别效率和识别准确性。

Abstract

In order to improve the stability of train operation, the fault diagnosis of traction power supply system was studied. According to the working principle and characteristics of the train power supply system, the relationship between the fault phenomenon and the origin was analyzed, and the characteristic signals used for fault diagnosis were extracted. A fault diagnosis model based on PSO optimized least squares support vector machine was established, and PCA algorithm was used to extract data characteristics as input of fault diagnosis model, and reduce input dimension. A variety of fault diagnosis models were used for comparative analysis. The research results showed that the PSO-LSSVM fault diagnosis model based on PCA algorithm has high recognition efficiency and accuracy.

关键词

列车供电故障诊断最小二乘支持向量机粒子群优化主成分分析接触网

Keywords

traction power supplyfault diagnosisleast squares support vector machineparticle swarm optimizationprincipal component analysiscatenary

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