{"title":"基于人工神经网络的GIS故障诊断系统","authors":"H. Ogi, H. Tanaka, Y. Akimoto, Y. Izui","doi":"10.1109/ANN.1991.213507","DOIUrl":null,"url":null,"abstract":"The authors present an artificial neural network (ANN) approach to a diagnostic system for a gas insulated switchgear (GIS). Firstly they survey the status of operational experience of failures in GISs and its diagnostic techniques. Secondly, they present how to acquire signal samples from the GIS and how to process them so as to be provided for an input layer of ANN. Finally they propose a decision-tree like network referred to as module neural network (MNN), and compare it with the well-known three-layered network, the straight forward neural network (SFNN).<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Fault diagnosis system for GIS using an artificial neural network\",\"authors\":\"H. Ogi, H. Tanaka, Y. Akimoto, Y. Izui\",\"doi\":\"10.1109/ANN.1991.213507\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors present an artificial neural network (ANN) approach to a diagnostic system for a gas insulated switchgear (GIS). Firstly they survey the status of operational experience of failures in GISs and its diagnostic techniques. Secondly, they present how to acquire signal samples from the GIS and how to process them so as to be provided for an input layer of ANN. Finally they propose a decision-tree like network referred to as module neural network (MNN), and compare it with the well-known three-layered network, the straight forward neural network (SFNN).<<ETX>>\",\"PeriodicalId\":119713,\"journal\":{\"name\":\"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ANN.1991.213507\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANN.1991.213507","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fault diagnosis system for GIS using an artificial neural network
The authors present an artificial neural network (ANN) approach to a diagnostic system for a gas insulated switchgear (GIS). Firstly they survey the status of operational experience of failures in GISs and its diagnostic techniques. Secondly, they present how to acquire signal samples from the GIS and how to process them so as to be provided for an input layer of ANN. Finally they propose a decision-tree like network referred to as module neural network (MNN), and compare it with the well-known three-layered network, the straight forward neural network (SFNN).<>
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