Dibo Wang, Ju Tang, R. Zhuo, Jun-yi Lin, Jian-rong Wu, Xiao-xing Zhang
{"title":"GIS PD模式识别的SA-SVM增量算法","authors":"Dibo Wang, Ju Tang, R. Zhuo, Jun-yi Lin, Jian-rong Wu, Xiao-xing Zhang","doi":"10.1109/ICHVE.2012.6357131","DOIUrl":null,"url":null,"abstract":"With changes in insulated defects, the environment, and so on, new partial discharge (PD) data are highly different from the original samples. It leads to a decrease in on-line recognition rate. Using ultra-high frequency (UHF) cumulative energy and its corresponding apparent discharge as inputs, a support vector machine (SVM) incremental method based on simulated annealing (SA) is constructed. Examples show that the new method speeds up the data update rate and improves the adaptability of the classifier.","PeriodicalId":6375,"journal":{"name":"2012 International Conference on High Voltage Engineering and Application","volume":"4 1","pages":"388-391"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"SA-SVM incremental algorithm for GIS PD pattern recognition\",\"authors\":\"Dibo Wang, Ju Tang, R. Zhuo, Jun-yi Lin, Jian-rong Wu, Xiao-xing Zhang\",\"doi\":\"10.1109/ICHVE.2012.6357131\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With changes in insulated defects, the environment, and so on, new partial discharge (PD) data are highly different from the original samples. It leads to a decrease in on-line recognition rate. Using ultra-high frequency (UHF) cumulative energy and its corresponding apparent discharge as inputs, a support vector machine (SVM) incremental method based on simulated annealing (SA) is constructed. Examples show that the new method speeds up the data update rate and improves the adaptability of the classifier.\",\"PeriodicalId\":6375,\"journal\":{\"name\":\"2012 International Conference on High Voltage Engineering and Application\",\"volume\":\"4 1\",\"pages\":\"388-391\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Conference on High Voltage Engineering and Application\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICHVE.2012.6357131\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on High Voltage Engineering and Application","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICHVE.2012.6357131","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SA-SVM incremental algorithm for GIS PD pattern recognition
With changes in insulated defects, the environment, and so on, new partial discharge (PD) data are highly different from the original samples. It leads to a decrease in on-line recognition rate. Using ultra-high frequency (UHF) cumulative energy and its corresponding apparent discharge as inputs, a support vector machine (SVM) incremental method based on simulated annealing (SA) is constructed. Examples show that the new method speeds up the data update rate and improves the adaptability of the classifier.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
