灰狼优化解决互联电力系统负荷频率控制:用GWO求解LFC问题

Dipayan Guha, P. Roy, Subrata Banerjee
{"title":"灰狼优化解决互联电力系统负荷频率控制:用GWO求解LFC问题","authors":"Dipayan Guha, P. Roy, Subrata Banerjee","doi":"10.4018/IJEOE.2016100104","DOIUrl":null,"url":null,"abstract":"In this article, a novel optimization algorithm called grey wolf optimization GWO with the theory of quasi-oppositional based learning Q-OBL is proposed for the first time to solve load frequency control LFC problem. An equal two-area thermal power system equipped with classical PID-controller is considered for this study. The power system network is modeled with governor dead band and time delay nonlinearities to get better insight of LFC system. 1% load perturbation in area-1 is considered to appraise the dynamic behavior of concerned power system. Integral time absolute error and least average error based fitness functions are defined for fine tuning of PID-controller gains employing the proposed method. An extensive comparative analysis is performed to establish the superiority of proposed algorithm over other recently published algorithms. Finally, sensitivity analysis is performed to show the robustness of the designed controller with system uncertainties.","PeriodicalId":246250,"journal":{"name":"Int. J. Energy Optim. Eng.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Grey Wolf Optimization to Solve Load Frequency Control of an Interconnected Power System: GWO Used to Solve LFC Problem\",\"authors\":\"Dipayan Guha, P. Roy, Subrata Banerjee\",\"doi\":\"10.4018/IJEOE.2016100104\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this article, a novel optimization algorithm called grey wolf optimization GWO with the theory of quasi-oppositional based learning Q-OBL is proposed for the first time to solve load frequency control LFC problem. An equal two-area thermal power system equipped with classical PID-controller is considered for this study. The power system network is modeled with governor dead band and time delay nonlinearities to get better insight of LFC system. 1% load perturbation in area-1 is considered to appraise the dynamic behavior of concerned power system. Integral time absolute error and least average error based fitness functions are defined for fine tuning of PID-controller gains employing the proposed method. An extensive comparative analysis is performed to establish the superiority of proposed algorithm over other recently published algorithms. Finally, sensitivity analysis is performed to show the robustness of the designed controller with system uncertainties.\",\"PeriodicalId\":246250,\"journal\":{\"name\":\"Int. J. Energy Optim. Eng.\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Energy Optim. Eng.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/IJEOE.2016100104\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Energy Optim. Eng.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/IJEOE.2016100104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

本文首次提出了一种新的优化算法——灰狼优化GWO,该算法基于准对偶学习的Q-OBL理论来解决负荷频率控制的LFC问题。本文研究了一种具有经典pid控制器的等二面积火电系统。利用调速器死区非线性和时滞非线性对电力系统网络进行建模,以更好地了解LFC系统。考虑1区1%的负荷摄动来评价系统的动态特性。定义了基于积分时间绝对误差和最小平均误差的适应度函数,用于pid控制器增益的微调。进行了广泛的比较分析,以确定所提出的算法优于其他最近发表的算法。最后,通过灵敏度分析验证了所设计控制器在系统不确定性下的鲁棒性。
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
Grey Wolf Optimization to Solve Load Frequency Control of an Interconnected Power System: GWO Used to Solve LFC Problem
In this article, a novel optimization algorithm called grey wolf optimization GWO with the theory of quasi-oppositional based learning Q-OBL is proposed for the first time to solve load frequency control LFC problem. An equal two-area thermal power system equipped with classical PID-controller is considered for this study. The power system network is modeled with governor dead band and time delay nonlinearities to get better insight of LFC system. 1% load perturbation in area-1 is considered to appraise the dynamic behavior of concerned power system. Integral time absolute error and least average error based fitness functions are defined for fine tuning of PID-controller gains employing the proposed method. An extensive comparative analysis is performed to establish the superiority of proposed algorithm over other recently published algorithms. Finally, sensitivity analysis is performed to show the robustness of the designed controller with system uncertainties.
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
Optimal Site Selection for Solar Photovoltaic Power Plant in North Eastern State of India using Hybrid MCDM Tools Security Constrained Optimal Reactive Power Dispatch Using Hybrid Particle Swarm Optimization and Differential Evolution Visible Light Communication System for Indoor Positioning Using Solar Cell as Receiver A New Central Control Scheme for Future Micro-Grid Systems Considering Variable Speed Drive Systems and Fuzzy Logic Control System Comparative Study of Two Different Converters with its Controller for Grid Connected WECS with PMSG
×
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