{"title":"Reduced Model for Power System State Estimation Using Artificial Neural Networks","authors":"Amamihe Onwuachumba, Yunhui Wu, M. Musavi","doi":"10.1109/GREENTECH.2013.69","DOIUrl":null,"url":null,"abstract":"In this paper a new technique using artificial neural networks for power system state estimation is presented. This method does not require network observability analysis and uses fewer measurement variables than conventional techniques. This approach has been successfully implemented on 6-bus and 18-bus power systems and the results are provided.","PeriodicalId":311325,"journal":{"name":"2013 IEEE Green Technologies Conference (GreenTech)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Green Technologies Conference (GreenTech)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GREENTECH.2013.69","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
In this paper a new technique using artificial neural networks for power system state estimation is presented. This method does not require network observability analysis and uses fewer measurement variables than conventional techniques. This approach has been successfully implemented on 6-bus and 18-bus power systems and the results are provided.
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