{"title":"基于蚁群优化Elman神经网络的接地网故障定位","authors":"Zhipeng Yi, Min-fang Peng, Hao He, Xianfeng Liu","doi":"10.1109/ICDMA.2012.97","DOIUrl":null,"url":null,"abstract":"In order to improve the accuracy and efficiency of the fault location of grounding grids, a new method combing ant colony algorithm (ACA) with Elman neural network is proposed. The method contrasts the voltages of the test points when the grounding grids is normal or not. The simulation results showes that the method can save time and improve accuracy.","PeriodicalId":393655,"journal":{"name":"International Conference on Digital Manufacturing and Automation","volume":"79 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Fault Locating of Grounding Grids Based on Ant colony Optimizing Elman Neural Network\",\"authors\":\"Zhipeng Yi, Min-fang Peng, Hao He, Xianfeng Liu\",\"doi\":\"10.1109/ICDMA.2012.97\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to improve the accuracy and efficiency of the fault location of grounding grids, a new method combing ant colony algorithm (ACA) with Elman neural network is proposed. The method contrasts the voltages of the test points when the grounding grids is normal or not. The simulation results showes that the method can save time and improve accuracy.\",\"PeriodicalId\":393655,\"journal\":{\"name\":\"International Conference on Digital Manufacturing and Automation\",\"volume\":\"79 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Digital Manufacturing and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMA.2012.97\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Digital Manufacturing and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMA.2012.97","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fault Locating of Grounding Grids Based on Ant colony Optimizing Elman Neural Network
In order to improve the accuracy and efficiency of the fault location of grounding grids, a new method combing ant colony algorithm (ACA) with Elman neural network is proposed. The method contrasts the voltages of the test points when the grounding grids is normal or not. The simulation results showes that the method can save time and improve accuracy.
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