Robust Fuzzy Neural Network Sliding Mode Control For Wind Turbine with a Permanent Magnet Synchronous Generator

Authors

  • K. Belamfedel Alaoui LESSI Laboratory, Department Of Physics, Faculty Of Sciences, Sidi Mohammed Ben Abdellah University, Morocco
  • S. Sefriti LESSI Laboratory, Department Of Physics, Faculty Of Sciences, Sidi Mohammed Ben Abdellah University, Morocco
  • I. Boumhidi LESSI Laboratory, Department Of Physics, Faculty Of Sciences, Sidi Mohammed Ben Abdellah University, Morocco

DOI:

https://doi.org/10.14738/tmlai.54.3336

Keywords:

variable speed wind turbine, permanent magnet synhronous generator, sliding mode control, fuzzy neural sliding mode control

Abstract

Abstract—In the present paper, we are interested in the contribution of wind power to the electricity supply in power systems of small sized isolated communities. A robust fuzzy neural sliding control (FNSC) is proposed to track the maximum power point for an isolated wind energy conversion system using a permanent magnet synchronous generator (PMSG) with a hysteresis rectifier connected to a DC load. The turbine is controlled by a sliding mode controller (SMC) to reach the maximum power level. The main objective of the control is to adjust the rectifier voltage to provide it to the DC load as well as to maintain the maximum power extraction. In presence of large uncertainties and wind speed variations, the traditional SMC produces the chattering phenomenon due to the higher needed gain. In order to reduce this gain, FNSC is used for the estimation of the unknown part, thus provide lower gain. The stability of the proposed FNSC is analyzed by Lyapunov theory, simulations results are presented and the proposed control performance is shown by the comparison with the conventional SMC.     

 

References

(1) J. F. Manwell, J. G. McGowan and A.L. Rogers, Wind Energy Explained: Theory, Design and Application, New York: John Wiley & Sons,2002.

(2) G. Ofualagba and E.U. Ubeku, “Wind energy conversion system-wind turbine modelling,” IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, pp. 1-8 , 2008.

(3) T. Burton, D.Sharpe,N. Jenkins and E. Bossanyi, Wind Energie Handbook. New York: John Wiley & Sons, 2001.

(4) V. I. Utkin, Sliding Modes in Control Optimization, Berlin: Springer-Verlag, 1992.

(5) J. J. Slotine, “Sliding controller design for non-linear systems,” International Journal of Control, vol. 40, pp. 421–434 , 1984.

(6) V. Utkin, J. Guldner and J. Shi, Sliding Mode Control in Electromechanical System, London: Taylor & Francis, 1999.

(7) E-M. Boufounas, J. Boumhidi and I. Boumhidi, “Optimal H∞ control without reaching phase for a variable speed wind turbine based on fuzzy neural network and APSO algorithm”, International Journal of Modelling, Identification and Control, Vol. 24, No. 2, pp. 100-109, 2015.

(8) H.P. Wang, A. Pintea, N. Christov, P. Borne and D. Popescu , “Modelling and recursive power control of horizontal variable speed wind turbines,” Journal of Control Engineering and Applied Informatics, vol. 14, no. 4, pp. 33-41, 2012.

(9) D .Cardenas, “Control of wind turbine using a switched reluance generator”. Phd thesis university of Nottingham.1996.

(10) E-M. Boufounas, J. Boumhidi and I. Boumhidi, “Optimal neural network sliding mode control for a variable speed wind turbine based on APSO algorithm”, Digests 2nd Annual Conf. IEEE on Complex Systems (WCCS’14) Morocco, p. 419-424, 2014.

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Published

2017-09-01

How to Cite

Alaoui, K. B., Sefriti, S., & Boumhidi, I. (2017). Robust Fuzzy Neural Network Sliding Mode Control For Wind Turbine with a Permanent Magnet Synchronous Generator. Transactions on Machine Learning and Artificial Intelligence, 5(4). https://doi.org/10.14738/tmlai.54.3336

Issue

Section

Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems