{"title":"Literature review on the control of brushless doubly-fed induction machine","authors":"None Ulrich Ngnassi Nguelcheu, None Ngasop Ndjiya, None Eric Duckler Kenmoe Fankem, None Aslain Brisco Ngnassi Djami, None Golam Guidkaya, None Alix Tioffo Dountio","doi":"10.30574/gjeta.2023.16.3.0186","DOIUrl":null,"url":null,"abstract":"In recent years, dual-fed brushless asynchronous generators (BDFIG) have attracted considerable attention in variable-speed drive applications due to their simple and robust structure, good operating characteristics, and low maintenance requirements. The purpose of controlling dual-fed brushless induction generators is to achieve better performance. However, various control techniques applied to this machine have shown their limits in case of sudden fluctuations in rotor speed, relatively long response time, poor stability and high performance sensitivity to parameter fluctuations. Given its difficulty, research has focused on the most advanced technology in the world: artificial intelligence (AI). The main objective of this article is to list all the control techniques that have been applied to the BDFIG. It appears from our study that genetic algorithms as well as the multilayer perceptron have not yet been applied for the control of BDFIG.","PeriodicalId":402125,"journal":{"name":"Global Journal of Engineering and Technology Advances","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Journal of Engineering and Technology Advances","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30574/gjeta.2023.16.3.0186","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, dual-fed brushless asynchronous generators (BDFIG) have attracted considerable attention in variable-speed drive applications due to their simple and robust structure, good operating characteristics, and low maintenance requirements. The purpose of controlling dual-fed brushless induction generators is to achieve better performance. However, various control techniques applied to this machine have shown their limits in case of sudden fluctuations in rotor speed, relatively long response time, poor stability and high performance sensitivity to parameter fluctuations. Given its difficulty, research has focused on the most advanced technology in the world: artificial intelligence (AI). The main objective of this article is to list all the control techniques that have been applied to the BDFIG. It appears from our study that genetic algorithms as well as the multilayer perceptron have not yet been applied for the control of BDFIG.