Improved Fuzzy Logic MPPT Controller of Stand-alone WECS-based PMSG under Stochastic Wind Environment
Main Article Content
This paper discusses the modeling and control of a Standalone WECS-based PMSG, the aim is to achieve an optimal operation of the studied WECS under a typically stochastic wind environment. The Maximum Power Point Tracking (MPPT) is guaranteed through the tracking of an optimal generator speed using an Improved Fuzzy Logic Controller (IFLC) based on intelligent algorithms. The effectiveness and the benefits of the proposed approach are demonstrated by numerical simulation using Matlab/SIMULINK. The obtained results indeed confirm a good tracking performance of the proposed controller.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Global Wind Energy Council (GWEC 2016), Global Wind Statistics, Report 10/02/2016, www.gwec.net.  Yu Ling, Xu Cai. Rotor current dynamics of doubly fed induction generators during grid voltage dip and rise. International Journal of Electrical Power & Energy Systems, Vol. 44, Issue 1, pp. 17-24, Jan2013.  Z. Z. Lan et al. Study on small signal stability improvement by PSS of the large-scale wind power integration. International Journal of Computer and Electrical Engineering 2013; 5(2):822–830.  Z. Z. Lan et al. Study on small signal stability improvement by PSS of the large-scale wind power integration. International Journal of Computer and Electrical Engineering 2013; 5(2):822–830.  I. Munteanu, I. B. Antoneta, C Nicolaos-Antonio, C. Emil. Optimal Control of Wind Energy Systems: Toward a Global Approach. Springer 2008.  A. Tabesh, R. Iravani. Frequency response analysis of torsional dynamics. IEEE Transactions on Power Systems 2004; 19(3):1430-1437.  M. Farmad, S. Farhangi, G.B. Gharehpetian, S. Afsharnia. Nonlinear controller design for IPC using feedback linearization method. International Journal of Electrical Power & Energy Systems 2013; 44(1):778-785.  B. Boukhezzara, H. Siguerdidjaneb. Nonlinear control with wind estimation of a DFIG variable speed wind turbine for power capture optimization. Energy Conversion and Management 2009; 50(4): 885–892.  S.K. Panda and P.K. Dash. Application of non linear control to switched relectance motors: a feedback linearization approch. IEE Proc Elect Power Appl. Vol. 143, no. 5, pp 371-379, Sept 1996.  S. El aimani, « Modélisation de différentes technologies d’éoliennes intégrées dans un réseau de moyenne tension », Thèse de doctorat de l’Ecole Centrale de Lille (ECL) Co-habilité avec L’université des sciences et technologies de Lille 1 (USTL) Spécialité : Génie électrique - Electronique - Automatique, 06 décembre 2004.  J. Wilkie, W.E. Leithead, C. Anderson, « Modelling of wind turbines by simple models » Wind engineering, vol. 14, No 4, 1990, pp. 247-274.  B. Bendib, F. Krim, H. Belmili, M.F. Almi, S. Bolouma. An intelligent MPPT approach based on neural-network voltage estimator and fuzzy controller, applied to a stand-alone PV system. 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE).  R Cheikh, H Belmili, S Drid, A Menacer, M Tiar. Fuzzy logic control algorithm of grid connected doubly fed induction generator driven by vertical axis wind turbine in variable speed. 2013 IEEE 3rd International Conference on Systems and Control (ICSC).