Optimal Reactive Power Optimization by Ant Colony Search Algorithm

Ibrahim Oumarou, Daozhuo Jiang, Cao Yijia
{"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.
基于蚁群搜索算法的最优无功优化
提出了一种用于电力系统无功优化和电压控制的蚁群搜索算法。ACSA是一种新的合作智能体方法,它的灵感来自于对真实蚁群行为的观察,研究蚁群的组队和觅食方法。因此,在ACSA中,一组被称为“蚂蚁”的合作智能体相互合作,以寻找更好的无功优化问题的解决方案。为了分析该搜索算法的效率和有效性,将该方法应用于IEEE 30,57,191(实际)测试总线系统,并与传统数学方法、遗传算法和自适应遗传算法的搜索结果进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信