{"title":"Discrimination of partial discharge from noise in XLPE cable lines using a neural network","authors":"G. Katsuta, H. Suzuki, H. Eshima, T. Endoh","doi":"10.1109/ANN.1993.264291","DOIUrl":null,"url":null,"abstract":"This paper describes an experimental study of the discrimination of partial discharge (PD) signals from external noise in a cross-linked polyethylene (XLPE) power cable by using a neural network (NN) system. Measurement of PD signal and external noise was carried out with a PD pulse recorder for a 66 kV XLPE cable with an artificial defect and a drill. The NN was a three-layer artificial neural system with feedforward connections, and its learning method was a backpropagation algorithm. Its input information was a combination of the discharge magnitude, the number of pulse counts, and the phase angle of applied voltage.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"120 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1993-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANN.1993.264291","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
This paper describes an experimental study of the discrimination of partial discharge (PD) signals from external noise in a cross-linked polyethylene (XLPE) power cable by using a neural network (NN) system. Measurement of PD signal and external noise was carried out with a PD pulse recorder for a 66 kV XLPE cable with an artificial defect and a drill. The NN was a three-layer artificial neural system with feedforward connections, and its learning method was a backpropagation algorithm. Its input information was a combination of the discharge magnitude, the number of pulse counts, and the phase angle of applied voltage.<>