Naziha Harrabi, M. Kharrat, M. Souissi, A. Aitouche
{"title":"Maximum power point tracking of a wind generation system based on T-S fuzzy model","authors":"Naziha Harrabi, M. Kharrat, M. Souissi, A. Aitouche","doi":"10.1109/STA.2015.7505127","DOIUrl":null,"url":null,"abstract":"The current paper is presenting a Takagi-Sugeno (T-S) fuzzy controller designed for a wind generation system in the objective of Maximum Power Point Tracking (MPPT). The considered Wind Energy Conversion System (WECS) is composed of a wind turbine and a Permanent Magnet Synchronous Generator (PMSG) associated to an AC-DC converter. First, the WECS fuzzy model is presented based on T-S approach. Next, the proposed controller is designed using Linear Matrix Inequalities (LMI) techniques and Lyapunov stability approach. Simulation results bring out the efficiency of the proposed scheme.","PeriodicalId":128530,"journal":{"name":"2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STA.2015.7505127","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
The current paper is presenting a Takagi-Sugeno (T-S) fuzzy controller designed for a wind generation system in the objective of Maximum Power Point Tracking (MPPT). The considered Wind Energy Conversion System (WECS) is composed of a wind turbine and a Permanent Magnet Synchronous Generator (PMSG) associated to an AC-DC converter. First, the WECS fuzzy model is presented based on T-S approach. Next, the proposed controller is designed using Linear Matrix Inequalities (LMI) techniques and Lyapunov stability approach. Simulation results bring out the efficiency of the proposed scheme.