文献类型：期刊文献

中文题名：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

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

作者：罗朋[1];李一峰[1];夏正龙[2];韩丽[3]

机构：[1]广东海洋大学电子与信息工程学院,广东湛江524088;[2]江苏师范大学电气工程与自动化学院,江苏徐州221116;[3]中国矿业大学信息与电气工程学院,江苏徐州221008

年份：2018

卷号：39

期号：2

起止页码：26

中文期刊名：电力电容器与无功补偿

外文期刊名：Power Capacitor & Reactive Power Compensation

收录：北大核心2017、北大核心

基金：江苏省自然科学基金资助项目(BK20160219)

语种：中文

中文关键词：H桥功率模块;IGBT;粒子群;最小二乘支持向量机;故障诊断

外文关键词：H-bridge power module; IGBT; particle swarm optimization; least squares support vector ma-chine; fault diagnosis

中文摘要：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。

外文摘要：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.