{"title":"人工神经网络在输电线路继电保护中的应用","authors":"S. Khaparde, P. Kale, S. Agarwal","doi":"10.1109/ANN.1991.213509","DOIUrl":null,"url":null,"abstract":"That the artificial neural network (ANN) can perform the pattern classification in excellent fashion is already established in the literature. The authors envisage the relay as a pattern classifying device. This opens a new dimension in relay philosophy which needs wide investigations. Keeping the microprocessor relay framework intact, the authors report the findings about the feasibility of using ANN in protection of transmission lines. The ADALINE model is explored for the application and is found to yield encouraging results. The input variables are quantified over the operating range which eases the arithmetics of the microprocessor. The training is performed in off-line mode and the converged weight matrix is stored for on-line use.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"36 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":"{\"title\":\"Application of artificial neural network in protective relaying of transmission lines\",\"authors\":\"S. Khaparde, P. Kale, S. Agarwal\",\"doi\":\"10.1109/ANN.1991.213509\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"That the artificial neural network (ANN) can perform the pattern classification in excellent fashion is already established in the literature. The authors envisage the relay as a pattern classifying device. This opens a new dimension in relay philosophy which needs wide investigations. Keeping the microprocessor relay framework intact, the authors report the findings about the feasibility of using ANN in protection of transmission lines. The ADALINE model is explored for the application and is found to yield encouraging results. The input variables are quantified over the operating range which eases the arithmetics of the microprocessor. The training is performed in off-line mode and the converged weight matrix is stored for on-line use.<<ETX>>\",\"PeriodicalId\":119713,\"journal\":{\"name\":\"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems\",\"volume\":\"36 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"30\",\"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.1991.213509\",\"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.1991.213509","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of artificial neural network in protective relaying of transmission lines
That the artificial neural network (ANN) can perform the pattern classification in excellent fashion is already established in the literature. The authors envisage the relay as a pattern classifying device. This opens a new dimension in relay philosophy which needs wide investigations. Keeping the microprocessor relay framework intact, the authors report the findings about the feasibility of using ANN in protection of transmission lines. The ADALINE model is explored for the application and is found to yield encouraging results. The input variables are quantified over the operating range which eases the arithmetics of the microprocessor. The training is performed in off-line mode and the converged weight matrix is stored for on-line use.<>