Control of PMSG Wind Energy Conversion System with TS Fuzzy State-Feedback Controller

Abstract:

This paper presents a Takagi-Sugeno (TS) fuzzy state-feedback controller based on a linear matrix inequality (LMI) approach for the permanent magnet synchronous generator of wind energy conversion system (PMSG-WECS). A dc/dc converter is considered to regulate the maximum power output of the system. To show its effectiveness, the dynamic model is replaced by the TS fuzzy model, which the proposed controller can be applied to the PMSG-WECS, while the controller gains can be obtained by solving set of a LMI approach. The proposed controller guarantees the stability of the system. Therefore, the performance of the proposed TS fuzzy state-feedback controller is assessed through the computer simulation.

• Periodical: Applied Mechanics and Materials (Volumes 446-447)
• Pages: 728-732
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.446-447.728
• Online since: November 2013
• Authors: Nikorn Kaewpraek, Wudhichai Assawinchaichote
• Keywords: LMI Approach, PMSG Wind Energy Conversion System, TS Fuzzy Model

References

[1] M. Chinchilla, S. Arnalts, and J. C. Burgos, in: Control of permanent magnet generators applied to variable speed wind energy systems connected to the grid, IEEE Trans. Ener. Conv., vol. 21, no. 1 (2006).

DOI: 10.1109/tec.2005.853735

[2] S. Li, D. C. Wunsch, E. A. O'Hair and M. G. Giesselmann, in: Using Neural networks to estimate wind turbine power generation, IEEE Trans. Ener. Conv., vol. 16, no. 3 (2001), pp.276-282.

DOI: 10.1109/60.937208

[3] H. Li, K. L. Shi and P. G. McLaren, in: Neural-network-based sensorless maximum wind energy capture with compensated power coefficient, IEEE Trans. Ind. Appl., vol. 41, no. 6 (2005) pp.1548-1556.

DOI: 10.1109/tia.2005.858282

[4] M. H. Ali, M. Park, I. Yu, T. Murata and J. Tamura, in: Improvement of wind generator stability by fuzzy logic controlled SMES, IEEE Trans. Ind. Appl., vol. 45, no. 3 (2009), pp.1045-1051.

DOI: 10.1109/tia.2009.2018901

[5] M. Azouz, A. Shaltout and M. A. L. Elshafei, in: Fuzzy logic control of wind energy system, Proc. Inter. Middle East Power Syst., id 311 (2010), pp.935-940.

[6] F. valenciaga and P. F. Puleston, in: Supervisor control for a stand-alone hybrid generation system using wind and photovoltaic energy, IEEE Trans. Ener. Convers., vol. 20, no. 2 (2005), pp.398-405.

DOI: 10.1109/tec.2005.845524

[7] W. Qi, J. Liu, X. Chen and P. D. Christofides, in: Supervisory predictive control of standalone wind/solar energy generation systems, IEEE Trans. Cont. Syst. Tech., (2010), pp.1-9.

DOI: 10.1109/tcst.2010.2041930

[8] D. J. Hill, and P. J. Moylan, in: Dissipative dynamical systems: Basic input-output and state properties, J. Franklin Inst., vol. 309 (1980), pp.327-357.

DOI: 10.1016/0016-0032(80)90026-5

[9] T. Takagi and M. Sugeno, in: Fuzzy identification of systems and its applications to model and control, IEEE Trans. on Syst. Management, vol. SMC-15, no. 1 (1985), pp.116-132.

DOI: 10.1109/tsmc.1985.6313399

[10] W. Assawinchaichote, S. K. Nguang and P. Shi, in: fuzzy state feedback control design for nonlinear systems with D stability constraints: An LMI approach, SciDirect. Math. Com. Sim. (2008), pp.514-531.

DOI: 10.1016/j.matcom.2007.07.002

[11] W. Assawinchaichote, in: A non-fragileoutput feedback controller for uncertain fuzzy dynamical systems with multiple time-scales, " Int. J. Computer, Communications&Control (2012), pp.8-19.

DOI: 10.15837/ijccc.2012.1.1419