{"title":"An Expert System for Incipient Fault Diagnosis and Condition Assessment in Transformers","authors":"H. Malik, T. Tarkeshwar, R. Jarial","doi":"10.1109/CICN.2011.27","DOIUrl":null,"url":null,"abstract":"The gases generated in oil filled transformer can be used for determination of incipient faults. Dissolved gas analysis (DGA) of transformer oil has been one of the most power full methods to detect the faults. The various methods such as liquid chromatography, acoustic analysis, and transformer function techniques are require some experience to interpret observations. The researchers have used artificial intelligence (AI) approach to encode these diagnostic techniques. This paper presents fuzzy-logic application and an overview of ANN techniques which can diagnose multiple faults in a transformer. Theoretical and practical fuzzy-logic (FL) information model and various researchers' ANN based experimental conclusion have been presented. This paper includes a demonstration of the application of the FL technique for transformer incipient fault diagnosis.","PeriodicalId":292190,"journal":{"name":"2011 International Conference on Computational Intelligence and Communication Networks","volume":"316 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Computational Intelligence and Communication Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CICN.2011.27","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 30
Abstract
The gases generated in oil filled transformer can be used for determination of incipient faults. Dissolved gas analysis (DGA) of transformer oil has been one of the most power full methods to detect the faults. The various methods such as liquid chromatography, acoustic analysis, and transformer function techniques are require some experience to interpret observations. The researchers have used artificial intelligence (AI) approach to encode these diagnostic techniques. This paper presents fuzzy-logic application and an overview of ANN techniques which can diagnose multiple faults in a transformer. Theoretical and practical fuzzy-logic (FL) information model and various researchers' ANN based experimental conclusion have been presented. This paper includes a demonstration of the application of the FL technique for transformer incipient fault diagnosis.