Power Transformer Fault Diagnosis by Using the Artificial Immune Support Vector Machines

摘要：

Dissolved gas analysis is an effective and important method for power transformer fault diagnosis. In order to improve the diagnostic accuracy of power transformer fault, the paper presents a method of hybrid intelligent algorithm of immune support vector machines. Considering the compactness characteristics of dissolved gas analysis data the achieved samples are pre-selected with the immune clustering analysis speed up the of the model parameters determination, the Support Vector Machine is used for Transformer Fault Diagnosis, and the grid search method based on cross-validation is chosen to determine model parameters. Comparison results show that the precision of fault diagnosis can be evidently improved.

关键词： Power transformer Fault diagnosis Araficial immune clustering Support vector machine

作者: REN Jing HUANG Jia-dong YU Yong-zhe

作者单位: Power Engineering North China Electric Power University Baoding, Hebei 071003, China Power Engineering North China Electric Power UniversityBaoding, Hebei 071003, China

会议类型: 国际会议

会议名称: 2009 Second International Conference on Intelligent Computation Technology and Automation(2009 第二届IEEE智能计算与自动化国际会议 ICICTA 2009)

会议地点: 长沙

会议语种:英文

页码: 2009-2012

在线出版日期: 2009-10-10（万方平台首次上网日期，不代表论文的发表时间）