A fuzzy control and neural network based rotor speed controller for maximum power point tracking in permanent magnet synchronous wind power generation system
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 , Zili Tao , Bo Hu , Meng Ye , Yingxiong Ou , 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