{"title":"神经网络在微机变压器继电保护中的应用","authors":"Li Yongli, H. Jiali, Duan Yuqian","doi":"10.1109/EMPD.1995.500810","DOIUrl":null,"url":null,"abstract":"A neural network method used to identify the operating states of transformers has been proposed and established. It is superior to the traditional transformer protective relays and can correctly identify, within half cycle from the fault inception, the internal faults, magnetizing inrush current state, external faults and switching on internal faults of a no-load transformer. In addition, this method has broad availability and high fault-tolerant ability. A lot of simulations have demonstrated its superiority.","PeriodicalId":447674,"journal":{"name":"Proceedings 1995 International Conference on Energy Management and Power Delivery EMPD '95","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Application of neural network to microprocessor-based transformer protective relaying\",\"authors\":\"Li Yongli, H. Jiali, Duan Yuqian\",\"doi\":\"10.1109/EMPD.1995.500810\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A neural network method used to identify the operating states of transformers has been proposed and established. It is superior to the traditional transformer protective relays and can correctly identify, within half cycle from the fault inception, the internal faults, magnetizing inrush current state, external faults and switching on internal faults of a no-load transformer. In addition, this method has broad availability and high fault-tolerant ability. A lot of simulations have demonstrated its superiority.\",\"PeriodicalId\":447674,\"journal\":{\"name\":\"Proceedings 1995 International Conference on Energy Management and Power Delivery EMPD '95\",\"volume\":\"59 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 1995 International Conference on Energy Management and Power Delivery EMPD '95\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EMPD.1995.500810\",\"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 1995 International Conference on Energy Management and Power Delivery EMPD '95","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EMPD.1995.500810","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of neural network to microprocessor-based transformer protective relaying
A neural network method used to identify the operating states of transformers has been proposed and established. It is superior to the traditional transformer protective relays and can correctly identify, within half cycle from the fault inception, the internal faults, magnetizing inrush current state, external faults and switching on internal faults of a no-load transformer. In addition, this method has broad availability and high fault-tolerant ability. A lot of simulations have demonstrated its superiority.