{"title":"Optimal Reactive Power Optimization by Ant Colony Search Algorithm","authors":"Ibrahim Oumarou, Daozhuo Jiang, Cao Yijia","doi":"10.1109/ICNC.2009.602","DOIUrl":null,"url":null,"abstract":"The paper presents an Ant Colony Search Algorithm(ACSA) for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents’ approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called “Ants” co-operates to find better solution for Reactive Power Optimization problem. To analyze the efficiency and effectiveness of this search algorithms,the proposed methods is applied to the IEEE 30, 57, 191(practical) test bus system and the results are compared to those of conventional mathematical methods, Genetic Algorithm and Adaptive Genetic Algorithm.","PeriodicalId":235382,"journal":{"name":"2009 Fifth International Conference on Natural Computation","volume":"97 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Fifth International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2009.602","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The paper presents an Ant Colony Search Algorithm(ACSA) for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents’ approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called “Ants” co-operates to find better solution for Reactive Power Optimization problem. To analyze the efficiency and effectiveness of this search algorithms,the proposed methods is applied to the IEEE 30, 57, 191(practical) test bus system and the results are compared to those of conventional mathematical methods, Genetic Algorithm and Adaptive Genetic Algorithm.
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