{"title":"Fault diagnosis in hydraulic turbine governor based on BP neural network","authors":"Yu Xiaohui, Liao Ruijin, Yao Chenguo","doi":"10.1109/ICEMS.2001.970680","DOIUrl":null,"url":null,"abstract":"This paper describes a new fault diagnosis model of the hydraulic turbine governing system with the advanced BPNN (backpropagation neural network), which consists of three layers: i.e. input layer (17 neurons), hidden layer, output layer (13 neurons). It is proved that the system can rind the faults correctly in GeZhouBa hydroelectric power station, and it can conduct the faults examination and repair of governing systems. So this diagnosis system should be applied widely in practice.","PeriodicalId":143007,"journal":{"name":"ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2001-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEMS.2001.970680","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper describes a new fault diagnosis model of the hydraulic turbine governing system with the advanced BPNN (backpropagation neural network), which consists of three layers: i.e. input layer (17 neurons), hidden layer, output layer (13 neurons). It is proved that the system can rind the faults correctly in GeZhouBa hydroelectric power station, and it can conduct the faults examination and repair of governing systems. So this diagnosis system should be applied widely in practice.
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