{"title":"The application of the data mining based on adaptive immune algorithm for power transformer, fault diagnosis","authors":"Lin Jikeng, Wu Congmin, Wang Dongtao","doi":"10.1109/CYBERC.2009.5342144","DOIUrl":null,"url":null,"abstract":"Adaptive Immune Algorithm based data mining (AIA-Data Mining) is presented for fault diagnosis of power transformer. The information entropy is used for the production of the initial population, which leads to convergence speed of the algorithm to be faster than that of the initial population produced by random. On the basis of that, the bi-level search Mechanism of the AIA further speeds up extraction of the decision-making table for the transformer fault diagnosis from the samples. Results from examples show that the method proposed is effective and feasible.","PeriodicalId":222874,"journal":{"name":"2009 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery","volume":"383 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CYBERC.2009.5342144","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Adaptive Immune Algorithm based data mining (AIA-Data Mining) is presented for fault diagnosis of power transformer. The information entropy is used for the production of the initial population, which leads to convergence speed of the algorithm to be faster than that of the initial population produced by random. On the basis of that, the bi-level search Mechanism of the AIA further speeds up extraction of the decision-making table for the transformer fault diagnosis from the samples. Results from examples show that the method proposed is effective and feasible.