A fuzzy control and neural network based rotor speed controller for maximum power point tracking in permanent magnet synchronous wind power generation system

IF 1.9 Q4 ENERGY & FUELS
Min Ding , Zili Tao , Bo Hu , Meng Ye , Yingxiong Ou , Ryuichi Yokoyama
{"title":"A fuzzy control and neural network based rotor speed controller for maximum power point tracking in permanent magnet synchronous wind power generation system","authors":"Min Ding ,&nbsp;Zili Tao ,&nbsp;Bo Hu ,&nbsp;Meng Ye ,&nbsp;Yingxiong Ou ,&nbsp;Ryuichi Yokoyama","doi":"10.1016/j.gloei.2023.10.004","DOIUrl":null,"url":null,"abstract":"<div><p>When the wind speed changes significantly in a permanent magnet synchronous wind power generation system, the maximum power point cannot be easily determined in a timely manner. This study proposes a maximum power reference signal search method based on fuzzy control, which is an improvement to the climbing search method. A neural network-based parameter regulator is proposed to address external wind speed fluctuations, where the parameters of a proportional-integral controller is adjusted to accurately monitor the maximum power point under different wind speed conditions. Finally, the effectiveness of this method is verified via Simulink simulation</p></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"6 5","pages":"Pages 554-566"},"PeriodicalIF":1.9000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Energy Interconnection","FirstCategoryId":"1087","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2096511723000798","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

Abstract

When the wind speed changes significantly in a permanent magnet synchronous wind power generation system, the maximum power point cannot be easily determined in a timely manner. This study proposes a maximum power reference signal search method based on fuzzy control, which is an improvement to the climbing search method. A neural network-based parameter regulator is proposed to address external wind speed fluctuations, where the parameters of a proportional-integral controller is adjusted to accurately monitor the maximum power point under different wind speed conditions. Finally, the effectiveness of this method is verified via Simulink simulation

基于模糊控制和神经网络的永磁同步风力发电系统最大功率点跟踪转子转速控制器
在永磁同步风力发电系统中,当风速发生显著变化时,无法及时确定最大功率点。本文提出了一种基于模糊控制的最大功率参考信号搜索方法,该方法是对爬升搜索方法的改进。提出了一种基于神经网络的参数调节器来解决外部风速波动问题,其中调整比例积分控制器的参数,以准确监测不同风速条件下的最大功率点。最后,通过Simulink仿真验证了该方法的有效性
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 求助全文
来源期刊
Global Energy Interconnection
Global Energy Interconnection Engineering-Automotive Engineering
CiteScore
5.70
自引率
0.00%
发文量
985
审稿时长
15 weeks
×
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