{"title":"Optimal reconfiguration of radial distribution networks for reducing voltage sags","authors":"R. Tapia-Juarez, E. Espinosa-Juárez, Mario Graff","doi":"10.1109/ICEEE.2013.6676027","DOIUrl":null,"url":null,"abstract":"This paper addresses the problem of optimal reconfiguration of distribution networks to tackle the problem of voltage sags. The networks have a radial topology and our procedure finds a reconfiguration where the expected occurrence of voltage sags is reduced. The paper describes the developed methodology, which is based on genetic algorithms; furthermore, the electric distribution system is represented as a graph, and the population is generated by providing the mesh information of the system. Case studies are presented for two IEEE test networks, demonstrating the effectiveness of the implemented methodology.","PeriodicalId":226547,"journal":{"name":"2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEE.2013.6676027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
This paper addresses the problem of optimal reconfiguration of distribution networks to tackle the problem of voltage sags. The networks have a radial topology and our procedure finds a reconfiguration where the expected occurrence of voltage sags is reduced. The paper describes the developed methodology, which is based on genetic algorithms; furthermore, the electric distribution system is represented as a graph, and the population is generated by providing the mesh information of the system. Case studies are presented for two IEEE test networks, demonstrating the effectiveness of the implemented methodology.