Dynamic Behavior Analysis for Optimally Tuned On-Grid DFIG Systems

S. Soued , H.S. Ramadan , M. Becherif
{"title":"Dynamic Behavior Analysis for Optimally Tuned On-Grid DFIG Systems","authors":"S. Soued ,&nbsp;H.S. Ramadan ,&nbsp;M. Becherif","doi":"10.1016/j.egypro.2019.04.035","DOIUrl":null,"url":null,"abstract":"<div><p>Metaheuristic Optimization Techniques (MOTs) such as the Artificial Bee Colony (ABC) algorithms and Grey Wolf Optimizer (GWO) can be conveniently used for reaching the Maximum Power Point Tracking (MPPT) of Wind Energy Conversion System (WECS). This paper presents an enhanced control strategy for both Rotor Side Converter (RSC) and Grid Side Converter (GSC) of the Doubly Fed Induction Generator (DFIG)-based WECS using the ABC and the GWO algorithms to ensure the MPPT for the WECS. The control strategy for the RSC and GSC are verified via 9 MW DFIG Wind Turbine (WT) using MATLAB<sup>TM</sup>/Simulink. The dynamic performance improvement of the DFIG depends on the appropriate choice of the optimal PI controllers’ gains. The numerical simulation results show the superiority of the proposed GWO-PI and the ABC-PI optimal controllers over the traditional PI regulators towards enhancing the DFIG system dynamic performance.</p></div>","PeriodicalId":11517,"journal":{"name":"Energy Procedia","volume":"162 ","pages":"Pages 339-348"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.egypro.2019.04.035","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Procedia","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1876610219313943","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

Abstract

Metaheuristic Optimization Techniques (MOTs) such as the Artificial Bee Colony (ABC) algorithms and Grey Wolf Optimizer (GWO) can be conveniently used for reaching the Maximum Power Point Tracking (MPPT) of Wind Energy Conversion System (WECS). This paper presents an enhanced control strategy for both Rotor Side Converter (RSC) and Grid Side Converter (GSC) of the Doubly Fed Induction Generator (DFIG)-based WECS using the ABC and the GWO algorithms to ensure the MPPT for the WECS. The control strategy for the RSC and GSC are verified via 9 MW DFIG Wind Turbine (WT) using MATLABTM/Simulink. The dynamic performance improvement of the DFIG depends on the appropriate choice of the optimal PI controllers’ gains. The numerical simulation results show the superiority of the proposed GWO-PI and the ABC-PI optimal controllers over the traditional PI regulators towards enhancing the DFIG system dynamic performance.

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
优化调谐并网DFIG系统的动态行为分析
人工蜂群(Artificial Bee Colony, ABC)算法和灰狼优化器(Grey Wolf Optimizer, GWO)等元启发式优化技术可以方便地实现风能转换系统的最大功率点跟踪(MPPT)。本文提出了一种基于双馈感应发电机(DFIG)的转子侧变换器(RSC)和栅格侧变换器(GSC)的增强控制策略,采用ABC和GWO算法来保证双馈感应发电机(DFIG)的转子侧变换器(RSC)和栅格侧变换器(GSC)的最大功率。利用matlab /Simulink对9mw DFIG风力发电机的RSC和GSC控制策略进行了验证。DFIG的动态性能改进取决于最佳PI控制器增益的适当选择。数值仿真结果表明,与传统的PI调节器相比,所提出的GWO-PI和ABC-PI最优控制器在提高DFIG系统动态性能方面具有优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Modular Energy Systems in Vehicular Applications A Pentacene -Based Organic Mis Structures Experimental Study of the Combined RES-Based Generators and Electric Storage Systems for Public Buildings An experimental study of the performance of the solar cell with heat sink cooling system Cooperative Operation of Parallel Connected Boost Converters for Low Voltage-High Power Applications: An Experimental Approach
×
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