Wei Wang, Chengrong Li, Wufeng Li, Lin Liu, Zezhong Wang, L. Ding
{"title":"Pattern recognition of single and composite partial discharge on generator stators","authors":"Wei Wang, Chengrong Li, Wufeng Li, Lin Liu, Zezhong Wang, L. Ding","doi":"10.1109/CEIDP.2001.963551","DOIUrl":null,"url":null,"abstract":"Partial discharge (PD) can indicate faults in insulation of generator stator windings, so it is important to identify the PD pattern for later inspection of generator stator windings. In this paper, some generator model bars were made and some typical PD phenomena in generator stator windings were simulated. PD tests were tried to find the features of composite PD. The data obtaind from the test were analyzed using statistical parameters and the spectrum of different types of PD were plotted to reveal the difference of different patterns of PD. A BP neural network was also used to recognize the PD patterns using some new input parameters.","PeriodicalId":112180,"journal":{"name":"2001 Annual Report Conference on Electrical Insulation and Dielectric Phenomena (Cat. No.01CH37225)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2001 Annual Report Conference on Electrical Insulation and Dielectric Phenomena (Cat. No.01CH37225)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEIDP.2001.963551","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Partial discharge (PD) can indicate faults in insulation of generator stator windings, so it is important to identify the PD pattern for later inspection of generator stator windings. In this paper, some generator model bars were made and some typical PD phenomena in generator stator windings were simulated. PD tests were tried to find the features of composite PD. The data obtaind from the test were analyzed using statistical parameters and the spectrum of different types of PD were plotted to reveal the difference of different patterns of PD. A BP neural network was also used to recognize the PD patterns using some new input parameters.