Decoupled PSO based rugged power flow method for loadability limit identification

Sandip Chakraborty, A. Manna, Animesh Gour, P. Acharjee
{"title":"Decoupled PSO based rugged power flow method for loadability limit identification","authors":"Sandip Chakraborty, A. Manna, Animesh Gour, P. Acharjee","doi":"10.1109/ICPEDC.2017.8081117","DOIUrl":null,"url":null,"abstract":"In this paper, PSO based robust load flow is proposed. In the proposed decoupled based PSO technique, the decoupling features among the power flow variables are taken into consideration. Very simple technique is applied to prompt the convergence. Keeping all inherent properties of evolutionary technique, improvement method is developed to attain better performances. To detect the stability margin, maximum loadability limit (MLL) is identified using the proposed method. The developed algorithm shows that it can provide satisfactory solutions under stressed situations when classical standard methods fail. To establish the effectiveness and efficiency, the proposed algorithm is compared with other methods.","PeriodicalId":145373,"journal":{"name":"2017 International Conference on Power and Embedded Drive Control (ICPEDC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Power and Embedded Drive Control (ICPEDC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPEDC.2017.8081117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this paper, PSO based robust load flow is proposed. In the proposed decoupled based PSO technique, the decoupling features among the power flow variables are taken into consideration. Very simple technique is applied to prompt the convergence. Keeping all inherent properties of evolutionary technique, improvement method is developed to attain better performances. To detect the stability margin, maximum loadability limit (MLL) is identified using the proposed method. The developed algorithm shows that it can provide satisfactory solutions under stressed situations when classical standard methods fail. To establish the effectiveness and efficiency, the proposed algorithm is compared with other methods.
基于解耦粒子群算法的负荷极限识别方法
本文提出了一种基于粒子群算法的鲁棒潮流算法。在基于解耦的粒子群算法中,考虑了潮流变量之间的解耦特性。用一种非常简单的技巧来促进收敛。在保留进化技术的所有固有特性的基础上,开发了改进方法以获得更好的性能。为了检测系统的稳定裕度,利用该方法确定了系统的最大负载极限(MLL)。结果表明,在经典标准方法失效的应力情况下,该算法能给出满意的解。为了验证该算法的有效性和效率,将该算法与其他方法进行了比较。
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