{"title":"Application of NSGA-II for reducing voltage deviations, power losses and control actions in a transmission power system","authors":"Y. R. Hernandez, T. Hiyama","doi":"10.1109/TD-ASIA.2009.5357016","DOIUrl":null,"url":null,"abstract":"A multi-objective genetic algorithm, based on NSGA-II, is implemented to find an optimal condition of minimum voltage deviations, minimum power losses and minimum number of control actions of a transmission network system. The system used as model is a IEEE 14-bus system. Generators, tap position of transformers and a shunt capacitor are the devices to controlling. The results show a succesful performance of the implemented algorithm. Also, different probability of mutation factors are compared and it is proved that a more important mutation factor can improve the velocity of convergence without fall into a random searching.","PeriodicalId":131589,"journal":{"name":"2009 Transmission & Distribution Conference & Exposition: Asia and Pacific","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Transmission & Distribution Conference & Exposition: Asia and Pacific","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TD-ASIA.2009.5357016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
A multi-objective genetic algorithm, based on NSGA-II, is implemented to find an optimal condition of minimum voltage deviations, minimum power losses and minimum number of control actions of a transmission network system. The system used as model is a IEEE 14-bus system. Generators, tap position of transformers and a shunt capacitor are the devices to controlling. The results show a succesful performance of the implemented algorithm. Also, different probability of mutation factors are compared and it is proved that a more important mutation factor can improve the velocity of convergence without fall into a random searching.