电测与仪表2012，Vol.49Issue(5)：11-16,6.
支持向量机参数优化及其在变压器故障诊断中的应用
Parameter Optimization for Support Vector Machine and Its Application to Fault Diagnosis of Power Transformers
尹金良 1朱永利1
作者信息
- 1. 华北电力大学电气与电子工程学院,河北保定071003
- 折叠
摘要
Abstract
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.关键词
支持向量机/交叉验证/遗传算法/参数优化/网格搜索/变压器故障诊断Key words
support vector machine/ cross validation/ genetic algorithm/ parameter optimization/ grid search/ power transformer fault diagnosis分类
信息技术与安全科学引用本文复制引用
尹金良,朱永利..支持向量机参数优化及其在变压器故障诊断中的应用[J].电测与仪表,2012,49(5):11-16,6.
