{"title":"Distribution network reconfiguration using genetic algorithm and load flow","authors":"P. L. Roux, J. Munda, Y. Hamam","doi":"10.1109/ASSCC.2012.6523339","DOIUrl":null,"url":null,"abstract":"There are various ways of reducing active power losses in distribution networks. Network reconfiguration is one of the most important methods. However, it can be a very difficult optimisation problem. The proposed Genetic Algorithm with Rank Weight selection is used to search the population for the optimum solution in the network. The chromosome is represented by a string of binary numbers (zeroes, 0s and ones, 1s). By opening one switch in each loop (fundamental loop approach), almost all the solutions will be feasible (radial networks). By doing this the chromosome will only consist of the open switches (Genotype); thus reducing the computation time. Constraints are used to filter the search by means of penalty factors, which guarantees the solution found by the Genetic Algorithm to be feasible.","PeriodicalId":341348,"journal":{"name":"2012 10th International Power & Energy Conference (IPEC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 10th International Power & Energy Conference (IPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASSCC.2012.6523339","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
There are various ways of reducing active power losses in distribution networks. Network reconfiguration is one of the most important methods. However, it can be a very difficult optimisation problem. The proposed Genetic Algorithm with Rank Weight selection is used to search the population for the optimum solution in the network. The chromosome is represented by a string of binary numbers (zeroes, 0s and ones, 1s). By opening one switch in each loop (fundamental loop approach), almost all the solutions will be feasible (radial networks). By doing this the chromosome will only consist of the open switches (Genotype); thus reducing the computation time. Constraints are used to filter the search by means of penalty factors, which guarantees the solution found by the Genetic Algorithm to be feasible.