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

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.%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 parame-ter 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 supportvec-tor machine and genetic algorithm least squares support vector machine,have such advantages as high di-agnostic accuracy and short diagnosis time as well as good practicability.