粒子群优化核极限学习机的变压器故障诊断

Transformer fault diagnosis based on particle swarm optimization and kernel-based extreme learning machine

核极限学习机(kernel-based extreme learning machine,KELM)在分类性能方面优于支持向量机(SVM),但仍存在参数敏感性的缺陷。针对这一缺陷,提出一种结合K折交叉验证(k-fold cross validation,K-CV)与粒子群优化(particle swarm optimization,PSO)的KELM分类器参数优化方法,将CV训练所得多个模型的平均准确率作为PSO的适应度评价函数,为KELM的参数优化提供评价标准。将该方法应用于变压器故障诊断中,充分利用数量有限的样本数据,提高KELM的泛化性能。实验结果表明,相比结合网格搜索(grid)的KELM、结合CV和Grid的KELM以及结合PSO的KELM,结合PSO的CV参数优化方法具有更好的性能。

The kernel-based extreme learning machine （KELM）has better classification performance than the SVM,but it still has the drawback of parameter sensitivity.For this defect,a method combining K-fold cross validation and particle swarm optimization （PSO）was proposed to optimize the parameter of KELM classi-fier,the average accuracy rate of the multi-ple models generated using the CV method was used as the fitness function of PSO to provide an evaluation criteria of KELM classifier.And this proposed method was used in the transformer fault diagnosis to make full use of the limited number of date samples and improve the generalization performance of KELM.Experimental result show that comparing with the method of KELM based on grid search,KELM based on CV and grid search and KELM based PSO,the proposed method has better per-formance.