基于支持向量数据描述的局部放电类型识别  

Partial Discharge Type Recognition Based on Support Vector Data Description

作　　者：唐炬[1] 林俊亦[1] 卓然[1] 陶加贵[1] 

机构地区：[1]重庆大学输配电装备及系统安全与新技术国家重点实验室,重庆400030

出　　处：《高电压技术》2013年第5期1046-1053,共8页High Voltage Engineering

基　　金：国家重点基础研究发展计划(973计划)(2009CB724506);国家自然科学基金(5177181)~~

摘　　要：电力设备内部绝缘缺陷发展往往会因环境条件的改变而变化,加之采集到的局部放电(PD)数据具有分散性和复杂性,导致传统绝缘故障识别方法效果不佳。为此,提出了一种用于气体绝缘组合电器(GIS)设备PD类型识别的支持向量数据描述(SVDD)算法。借鉴支持向量机(SVM)算法中最大化"间隔"的思想,建立了这种优化的支持向量数据描述(OR-SVDD)算法。该算法采用多分类方法中的"一对多"原理,用以解决对传统绝缘故障出现的识别率低、误识别、漏识别以及识别时间长等问题。通过仿真与实验结果表明,OR-SVDD算法能够对所有的数据进行正确描述,自动辨识拒识对象,训练时间低于传统的SVM算法,并具有较高的识别率,在电力设备在线监测与局部放电模式识别领域有良好的应用前景。Traditional methods of insulation defect recognition often perform not well due to the changes in defect development influenced by changing environmental conditions,as well as the scattered and complex partial discharge（PD） data obtained.Therefore,we proposed a support vector data description（SVDD） algorithm for PD pattern recognition of gas insulated switchgear（GIS）.Based on the principle of maximum interval of support vector machine（SVM） and the one-to-multiple principle of multiple classification methods,the optimal radius SVDD（OR-SVDD） algorithm was proposed to solve the problems of traditional recognition methods,including low recognition rate,recognition error,recognition miss,and long recognition time.Simulation and experiments prove that the OR-SVDD algorithm for identification performs better than the traditional SVM algorithm： in a comparatively shorter time and with higher recognition rate,all data objects are described correctly,while the outlying objects are recognized in the target data objects effectively.Therefore,it is concluded that the OR-SVDD algorithm has a good application prospects in both on-line monitoring of power equipment and PD pattern recognition.

关 键 词：局部放电 支持向量机 SVM 支持向量数据描述 SVDD 拒识 模式识别 

分 类 号：TM855[电气工程—高电压与绝缘技术]