Evaluation of fuzzy-based maximum power-tracking in wind energy conversion systems
Evaluation of fuzzy-based maximum power-tracking in wind energy conversion systems
- Author(s): M. Azzouz ; A.-l. Elshafei ; H. Emara
- DOI: 10.1049/iet-rpg.2010.0102
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- Author(s): M. Azzouz 1 ; A.-l. Elshafei 1 ; H. Emara 1
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View affiliations
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Affiliations:
1: Faculty of Engineering, Cairo University, Giza, Egypt
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Affiliations:
1: Faculty of Engineering, Cairo University, Giza, Egypt
- Source:
Volume 5, Issue 6,
November 2011,
p.
422 – 430
DOI: 10.1049/iet-rpg.2010.0102 , Print ISSN 1752-1416, Online ISSN 1752-1424
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.
Inspec keywords: direct energy conversion; variable structure systems; fuzzy systems; wind power; adaptive control; PI control; fuzzy set theory; search problems
Other keywords:
Subjects: Optimisation techniques; Combinatorial mathematics; Other direct energy conversion; Optimisation techniques; Self-adjusting control systems; Multivariable control systems; Combinatorial mathematics; Control of electric power systems
References
-
-
1)
- V. Akhmatov . Variable-speed wind turbines with doubly-fed induction generators, part I: modeling in dynamic simulation tools. Wind Eng. , 2 , 85 - 108
-
2)
- P.P. Angelov , D.P. Filev . An approach to online identification of Takagi–Sugeno fuzzy models. IEEE Trans. Syst. Man Cybern. B , 1 , 484 - 498
-
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)
- T. Thiringer , J. Linders . Control by variable rotor speed of a fixed pitch wind turbine operating in a wide speed range. IEEE Trans. Energy Conv. , 520 - 526
-
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)
- T. Tanaka , T. Toumiya . Output control by Hill-Climbing method for a small wind power generating system. Renew. Energy , 4 , 387 - 400
-
7)
- V. Calderaro , V. Galdi , A. Piccolo , P. Siano . A fuzzy controller for maximum energy extraction from variable speed wind power generation systems. Electr. Power Syst. Res. , 1109 - 1118
-
8)
- C. Nichita , D. Luca , B. Dakyo , E. Ceanga . Large band simulation of the wind speed for real time wind turbine simulators. IEEE Trans. Energy Convers. , 4 , 523 - 529
-
9)
- S.L. Chiu . Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. , 267 - 278
-
10)
- S. Heier . (1998) Grid integration of wind energy conversion systems.
-
11)
- R. Pena , J.C. Clare , G.M. Asher . Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind-energy generation. IEE Proc., Electr. Power Appl. , 231 - 241
-
12)
- http://www.mathworks.com/help/toolbox/physmod/powersys/ref/windturbinedoublyfedinductiongeneratorphasortype.html, accessed September 2010.
-
13)
- L. Hsu , R. Costa . Analysis and design of I/O based variable structure adaptive control. IEEE Trans. Autom. Control , 1 , 4 - 21
-
14)
- L.Y. Pao , K.E. Johnson . (2009) A tutorial on the dynamic and control of wind turbines and wind farms.
-
15)
- M.G. Simoes , K. Bose , R.J. Spiegel . Design and performance evaluation of a fuzzy-logic-based variable-speed wind generation system. IEEE Trans. Ind. Appl. , 956 - 964
-
16)
- J.-S. Jang . ANFIS: adaptive-network-based fuzzy-inference system. IEEE Trans. Syst. Man Cybern. , 3 , 665 - 685
-
17)
- Q. Wang , L.C. Chang . An intelligent maximum power extraction algorithm for inverter-based variable speed wind turbine systems. IEEE Trans. Power Electron. , 1242 - 1249
-
18)
- R. Chedid , F. Mrad , M. Basma . Intelligent control of a class of wind energy conversion systems. IEEE Trans. Energy Convers. , 1597 - 1604
-
19)
- http://www.mathworks.com/help/toolbox/physmod/powersys/ug/f12-6566.html, accessed September 2010.
-
20)
- A.L. Elshafei , K.A. El-Metwally , A.A. Shaltout . A variable-structure adaptive fuzzy-logic stabilizer for single and multi-machine power systems. Control Eng. Pract. , 413 - 423
-
21)
- Hui, J.: `An adaptive control algorithm for maximum power point tracking for wind energy conversion systems', 2008, Master, Queen's University, Canada.
-
1)