支持向量机参数优化及其在变压器故障诊断中的应用

支持向量机(Support Vector Machines,SVM)分类器在变压器故障诊断中取得了较好的效果,然而对其性能起关键作用的参数选择,却没有理论依据或有效方法。鉴于交叉验证(Cross validation,CV)是模型性能评估和模型选择的有效方法,而遗传算法(Genetic Algorithm,GA)可以实现全局寻优,将CV与GA方法相结合用来选取SVM分类器参数。该方法采用GA方法对SVM分类器参数进行优化,利用CV构造GA适应度函数,为SVM分类器参数选择提供评价标准。并将其应用于变压器故障诊断,从而充分利用有限的变压器故障样本数据,提高SVM分类器的推广性。实例分析表明同Grid与SVM相结合,CV、Grid与SVM相结合及GA与SVM相结合的方法相比,所提方法具有更好的效果。

Parameter Optimization for Support Vector Machine and Its Application to Fault Diagnosis of Power Transformers

Support Vector Machine classifier is an effective method for the fault diagnosis of power transformer.However,there is no theoretical basis or effective methods to select appropriate SVM classifier parameters which have a great effect on the performance of SVM classifier.Because genetic algorithm(GA) is one of the most common optimization techniques and cross validation(CV) is widely accepted a standard procedure for choosing proper model parameters and estimating model performance.In this paper,SVM classifier with parameters optimized by GA combined with cross validation is applied to power transformer fault diagnosis(CVGA-SVM).In the method,GA is used to search for the optimal parameters of the SVM classifiers and CV is used to estimate the performance of SVM classifier determined by difference parameters and learning set.The method can make full use of the limited power transformers fault sample data and improve the generalization of SVM classifier.Experimental results show that CVGA-SVM has more excellent diagnostic performance compared with the SVM classifier with parameter optimized by Grid,Grid combined with CV and GA.