基于pso的CPSO参数调整方法

I. Ziari, A. Jalilian
{"title":"基于pso的CPSO参数调整方法","authors":"I. Ziari, A. Jalilian","doi":"10.1109/ICHQP.2010.5625405","DOIUrl":null,"url":null,"abstract":"This paper introduces a modified particle swarm optimization (MPSO) algorithm which gets benefit from all remarkable advantages of conventional PSO (CPSO) in addition to lower possibility of catching in premature convergence and higher accuracy. In this paper, influence of CPSO parameters changes on the output accuracy is firstly represented and studied; then, a modified PSO called MPSO is studied to calculate these parameters optimally and improve the premature convergence problem along with the accuracy. In the proposed approach, CPSO parameters are determined using another CPSO algorithm in which parameters are selected typically. To evaluate the proposed MPSO, a 6-bus power system is considered in which two nonlinear loads are located as harmonics generators. A Comparison between the results of MPSO and those of CPSO and genetic algorithm (GA) is used to demonstrate the applicability and effectiveness of the MPSO-based algorithm and its superiority over other techniques.","PeriodicalId":180078,"journal":{"name":"Proceedings of 14th International Conference on Harmonics and Quality of Power - ICHQP 2010","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A PSO-based approach to adjust CPSO parameters\",\"authors\":\"I. Ziari, A. Jalilian\",\"doi\":\"10.1109/ICHQP.2010.5625405\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces a modified particle swarm optimization (MPSO) algorithm which gets benefit from all remarkable advantages of conventional PSO (CPSO) in addition to lower possibility of catching in premature convergence and higher accuracy. In this paper, influence of CPSO parameters changes on the output accuracy is firstly represented and studied; then, a modified PSO called MPSO is studied to calculate these parameters optimally and improve the premature convergence problem along with the accuracy. In the proposed approach, CPSO parameters are determined using another CPSO algorithm in which parameters are selected typically. To evaluate the proposed MPSO, a 6-bus power system is considered in which two nonlinear loads are located as harmonics generators. A Comparison between the results of MPSO and those of CPSO and genetic algorithm (GA) is used to demonstrate the applicability and effectiveness of the MPSO-based algorithm and its superiority over other techniques.\",\"PeriodicalId\":180078,\"journal\":{\"name\":\"Proceedings of 14th International Conference on Harmonics and Quality of Power - ICHQP 2010\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 14th International Conference on Harmonics and Quality of Power - ICHQP 2010\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICHQP.2010.5625405\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 14th International Conference on Harmonics and Quality of Power - ICHQP 2010","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICHQP.2010.5625405","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

本文提出了一种改进的粒子群优化算法(MPSO),该算法具有传统粒子群优化算法的所有显著优点,并且具有较低的捕获早收敛的可能性和较高的精度。本文首先研究了CPSO参数变化对输出精度的影响;然后,研究了一种改进的粒子群算法(MPSO),对这些参数进行最优计算,改善了粒子群算法的早熟收敛问题和精度。在所提出的方法中,使用另一种典型参数选择的CPSO算法确定CPSO参数。为了评估所提出的MPSO,考虑了一个6母线电力系统,其中两个非线性负载被定位为谐波发生器。通过将MPSO算法与遗传算法(GA)和CPSO算法的结果进行比较,证明了基于MPSO算法的适用性和有效性,以及其相对于其他技术的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A PSO-based approach to adjust CPSO parameters
This paper introduces a modified particle swarm optimization (MPSO) algorithm which gets benefit from all remarkable advantages of conventional PSO (CPSO) in addition to lower possibility of catching in premature convergence and higher accuracy. In this paper, influence of CPSO parameters changes on the output accuracy is firstly represented and studied; then, a modified PSO called MPSO is studied to calculate these parameters optimally and improve the premature convergence problem along with the accuracy. In the proposed approach, CPSO parameters are determined using another CPSO algorithm in which parameters are selected typically. To evaluate the proposed MPSO, a 6-bus power system is considered in which two nonlinear loads are located as harmonics generators. A Comparison between the results of MPSO and those of CPSO and genetic algorithm (GA) is used to demonstrate the applicability and effectiveness of the MPSO-based algorithm and its superiority over other techniques.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Harmonic domain modeling of transformer nonlinear characteristic with piece-wise approximation Classification of Power Quality disturbances using the iterative Hilbert Huang Transform The problems of centralized decrease of harmonic voltages in the HV networks with distributed nonlinear loads Elimination of harmonics using multi-pulse rectifiers Power quality in public lighting systems
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1