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Evaluation of fuzzy-based maximum power-tracking in wind energy conversion systems

Evaluation of fuzzy-based maximum power-tracking in wind energy conversion systems

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Fuzzy logic is a convenient approach to construct maximum power-point-tracking algorithms. A new scheme composed of two fuzzy systems is proposed here. The first fuzzy system is based on a modified hill climb search algorithm to conclude the power set-point. The second fuzzy system is an adaptive proportional integral-like controller that uses a variable structure tuning algorithm to track the power set-point. Simulations show that the proposed scheme can improve the system efficiency.

References

    1. 1)
    2. 2)
    3. 3)
      • Rawn, B.: `Wind energy conversion systems as power filter: a control methodology', 2004, Master, University of Toronto, Graduate Department of Electric and Computer Engineering, Canada.
    4. 4)
    5. 5)
      • Miller, N.W., Sanchez-Gasca, J.J., Price, W.W., Delmerico, R.W.: `Dynamic modeling of GE 1.5 and 3.6 MW wind turbine-generators for stability simulations', GE Power Systems Energy Consulting, IEEE WTG Modeling Panel, 2003, 3, p. 1977–1983.
    6. 6)
    7. 7)
    8. 8)
    9. 9)
      • S.L. Chiu . Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. , 267 - 278
    10. 10)
      • S. Heier . (1998) Grid integration of wind energy conversion systems.
    11. 11)
    12. 12)
      • http://www.mathworks.com/help/toolbox/physmod/powersys/ref/windturbinedoublyfedinductiongeneratorphasortype.html, accessed September 2010.
    13. 13)
    14. 14)
      • L.Y. Pao , K.E. Johnson . (2009) A tutorial on the dynamic and control of wind turbines and wind farms.
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
      • http://www.mathworks.com/help/toolbox/physmod/powersys/ug/f12-6566.html, accessed September 2010.
    20. 20)
    21. 21)
      • Hui, J.: `An adaptive control algorithm for maximum power point tracking for wind energy conversion systems', 2008, Master, Queen's University, Canada.
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