IGBT Fault Diagnosis in H-bridge Power Module by Particle Swarm Optimization Least Squares Support Vector Machine

LUO Peng; LI Yifeng; XIA Zhenglong; HAN Li

doi:10.14044/j.1674-1757.pcrpc.2018.02.005

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IGBT Fault Diagnosis in H-bridge Power Module by Particle Swarm Optimization Least Squares Support Vector Machine

更新时间：2024-09-14

- IGBT Fault Diagnosis in H-bridge Power Module by Particle Swarm Optimization Least Squares Support Vector Machine
- POWER CAPACITOR & REACTIVE POWER COMPENSATION Vol. 39, Issue 2, Pages: 26-31(2018)
- 作者机构：
  
  1. 广东海洋大学电子与信息工程学院
  2. 江苏师范大学电气工程与自动化学院
  3. 中国矿业大学信息与电气工程学院
- 作者简介：
- 基金信息：
- DOI：10.14044/j.1674-1757.pcrpc.2018.02.005
  CLC： TN322.8
- Published：2018，
- 稿件说明：
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[1]罗朋,李一峰,夏正龙,韩丽.利用粒子群优化最小二乘支持向量机诊断H桥功率模块IGBT故障[J].电力电容器与无功补偿,2018,39(02):26-31.

LUO PENG, LI YIFENG, XIA ZHENGLONG, et al. IGBT Fault Diagnosis in H-bridge Power Module by Particle Swarm Optimization Least Squares Support Vector Machine. [J]. Power capacitor & reactive power compensation, 2018, 39(2): 26-31.
[1]罗朋,李一峰,夏正龙,韩丽.利用粒子群优化最小二乘支持向量机诊断H桥功率模块IGBT故障[J].电力电容器与无功补偿,2018,39(02):26-31. DOI： 10.14044/j.1674-1757.pcrpc.2018.02.005.

LUO PENG, LI YIFENG, XIA ZHENGLONG, et al. IGBT Fault Diagnosis in H-bridge Power Module by Particle Swarm Optimization Least Squares Support Vector Machine. [J]. Power capacitor & reactive power compensation, 2018, 39(2): 26-31. DOI： 10.14044/j.1674-1757.pcrpc.2018.02.005.

摘要

为了提高H桥功率模块中IGBT故障诊断的准确性

提出将粒子群优化最小二乘支持向量机用于H桥功率模块中IGBT故障诊断。分析了功率模块中可供采集的信号

将H桥直流侧电容电压作为故障的原始信号。通过小波多分辨率提取故障特征。采用粒子群算法优化最小二乘支持向量机中的核函数和正则化参数。通过仿真实验表明

粒子群优化最小二乘支持向量与默认参数最小二乘支持向量机、粒子群优化支持向量机和遗传算法优化最小二乘支持向量机相比

诊断准确率高和诊断时间短等优点

具有很好的实用性。

Abstract

For improving the accuracy of IGBT fault diagnosis in H-bridge power modules

a particle swarm optimization least squares support vector machine is proposed for IGBT fault diagnosis in H-bridge power modules. The signal to be collected in the power module is analyzed

and the capacitive voltage at the DC side of H-bridge is used as original signal of the fault. The fault features are extracted by wavelet multi-resolution. The PSO algorithm is used to optimize the kernel function and the regularization parameter in the least squares support vector machine. It is shown by the simulation experiment that

compared with the default parameters least squares support vector machine

particle swarm optimization support vector machine and genetic algorithm least squares support vector machine

have such advantages as high diagnostic accuracy and short diagnosis time as well as good practicability.

关键词

H桥功率模块IGBT粒子群最小二乘支持向量机故障诊断

Keywords

H-bridge power moduleIGBTparticle swarm optimizationleast squares support vector machinefault diagnosis

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