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An optimal fuzzy PI controller to capture the maximum power for variable-speed wind turbines

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Abstract

Wind energy conversion systems can work by fixed and variable speed using the power electronic converters. The variable-speed type is more desirable because of its ability to achieve maximum efficiency at all wind speeds. The main operational region for wind turbines according to wind speed is divided into partial load and full load. In the partial-load region, the main goal is to maximize the power captured from the wind. This goal can be achieved by controlling the generator torque such that the optimal tip speed ratio is tracked. Since the wind turbine systems are nonlinear in nature and due to modeling uncertainties, this goal is difficult to be achieved in practice. The proportional-integral (PI) controller, due to its robustness and simplicity, is very often used in practical applications, but finding its optimal gains is a challenging task. In this paper, to cope with nonlinearities and at the same time modeling uncertainties of wind turbines, a PI torque controller is proposed such that its optimal gains are derived via a novel scheme based on particle swarm optimization algorithm and fuzzy logic theory. The proposed method is applied to a 5-MW wind turbine model. The simulation results show the effectiveness of the proposed method in capturing maximum power in the partial-load region while coping well with nonlinearities and uncertainties.

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References

  1. The European Wind Energy Association (2012) Global statistics. http://www.ewea.org/index.php?id=1487. Accessed 24 June 2012

  2. Meharrar A, Tioursi M, Hatti M, Stambouli AB (2011) A variable speed wind generator maximum power tracking based on adaptative neuro-fuzzy inference system. Expert Syst Appl 38:7659–7664

    Article  Google Scholar 

  3. Lin WM, Hong CM (2010) Intelligent approach to maximum power point tracking control strategy for variable-speed wind turbine generation system. Energy 35:2440–2447

    Article  Google Scholar 

  4. Caliao ND (2011) Dynamic modelling and control of fully rated converter wind turbines. Renew Energy 36:2287–2297

    Article  Google Scholar 

  5. Boukhezzar B, Siguerdidjane H (2009) Nonlinear control with wind estimation of a DFIG variable speed wind turbine for power capture optimization. Energy Convers Manag 50:885–892

    Article  Google Scholar 

  6. Boukhezzar B, Lupu L, Siguerdidjane H, Hand M (2007) Multivariable control strategy for variable speed, variable pitch wind turbine. Renew Energy 32:1273–1287

    Article  Google Scholar 

  7. Narayana M, Putrus GA, Jovanovic M, Leung PS, McDonald S (2012) Generic maximum power point tracking controller for small-scale wind turbines. Renew Energy 44:72–79

    Article  Google Scholar 

  8. Abdullah MA, Yatim AHM, Tan CW, Saidur R (2012) A review of maximum power point tracking algorithms for wind energy systems. Renew Sustain Energy Rev 16:3220–3227

    Article  Google Scholar 

  9. Liao M, Dong L, Jin L, Wang S (2009) Study on rotational speed feedback torque control for wind turbine generator system. In: The proceedings of the international conference on energy and environment technology, pp 853–856

  10. Leithead WE, Connor B (2000) Control of variable speed wind turbines: design task. Int J Control 73:1189–1212

    Article  MathSciNet  MATH  Google Scholar 

  11. Munteanu I, Cutululis N, Bratcu A, Ceanga E (2005) Optimization of variable speed wind power systems based on a LQG approach. Control Eng Pract 13:903–912

    Article  Google Scholar 

  12. Connor D, Iyer SN, Leithead WE, Grimble MJ (1992) Control of horizontal axis wind turbine using H∞ control. In: The proceedings of the IEEE conference on control applications, vol 1, pp 117–122

  13. Vihriälä H, Perälä R, Mäkilä P, Söderlund L (2001) A gearless wind power drive: part 2: performance of control system. In: The proceedings of the European wind energy conference, vol 1, pp 1090–1093

  14. Beltran B, Ahmed-Ali T, Benbouzid M (2009) High-order sliding-mode control of variable-speed wind turbines. IEEE Trans Ind Electron 56:3314–3321

    Article  Google Scholar 

  15. Boukhezzar B, Siguerdidjane H (2011) Nonlinear control of a variable-speed wind turbine using a two-mass model. IEEE Trans Energy Convers 26:149–162

    Article  Google Scholar 

  16. Bououden S, Chadli M, Filali S, El Hajjaji A (2012) Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach. Renew Energy 37:434–439

    Article  Google Scholar 

  17. Mohamed AZ, Eskander MN, Ghali FA (2001) Fuzzy logic control based maximum power tracking of a wind energy system. Renew Energy 23:235–245

    Article  Google Scholar 

  18. Sargolzaei J, Kianifar A (2010) Neuro-fuzzy modeling tools for estimation of torque in Savonius rotor wind turbine. Adv Eng Softw 41:619–626

    Article  MATH  Google Scholar 

  19. Chadli M, El Hajjaji A (2010) Wind energy conversion systems control using T-S fuzzy modeling. In: The proceedings of the mediterranean conference on control and automation, pp 1365–1370

  20. Kamal E, Bayart M, Aitouche A (2011) Robust control of wind energy conversion systems. In: The proceedings of the international conference on communications, computing and control applications, pp 1–6

  21. Song Z, Shi T, Xia C, Chen W (2012) A novel adaptive control scheme for dynamic performance improvement of DFIG-based wind turbines. Energy 38:104–117

    Article  Google Scholar 

  22. Calderaro V, Galdi V, Piccolo A, Siano P (2008) A fuzzy controller for maximum energy extraction from variable speed wind power generation systems. Electr Power Syst Res 78:1109–1118

    Article  Google Scholar 

  23. Galdi V, Piccolo A, Siano P (2009) Exploiting maximum energy from variable speed wind power generation systems by using an adaptive Takagi–Sugeno–Kang fuzzy model. Energy Convers Manag 50:413–421

    Article  Google Scholar 

  24. Lin W-M, Hong C-M, Cheng F-S (2010) Fuzzy neural network output maximization control for sensorless wind energy conversion system. Energy 35:592–601

    Article  Google Scholar 

  25. Lin W-M, Hong C-M (2010) Intelligent approach to maximum power point tracking control strategy for variable-speed wind turbine generation system. Energy 35:2440–2447

    Article  Google Scholar 

  26. Lin W-M, Hong C-M, Cheng F-S (2011) Design of intelligent controllers for wind generation system with sensorless maximum wind energy control. Energy Convers Manag 52:1086–1096

    Article  Google Scholar 

  27. Kusiak A, Zheng H (2010) Optimization of wind turbine energy and power factor with an evolutionary computation algorithm. Energy 35:1324–1332

    Article  Google Scholar 

  28. Lin W-M, Hong C-M, Cheng F-S (2010) On-line designed hybrid controller with adaptive observer for variable-speed wind generation system. Energy 35:3022–3030

    Article  Google Scholar 

  29. Kusiak A, Li W, Song Z (2010) Dynamic control of wind turbines. Renew Energy 35:456–463

    Article  Google Scholar 

  30. Wu D, Xu L, Ji Z (2009) Fuzzy adaptive control for wind energy conversion system based on model reference. In: The proceedings of the Chinese conference on control and decision, pp 1783–1787

  31. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: The proceedings of the IEEE international conference on neural networks, vol 4, pp 1942–1948

  32. Shi Y, Eberhart R (1998) Parameter selection in particle swarm optimization. In: The proceedings of international conference on evolutionary programming, pp 591–601

  33. Cox E (1993) Adaptive fuzzy systems. IEEE Spectr 30:27–31

    Article  Google Scholar 

  34. Dong X, Zhao Y, Xu Y, Zhang Z, Shi P (2011) Design of PSO fuzzy neural network control for ball and plate system. Int J Innov Comput Inf Control 7:7091–7104

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Islamic Azad University, South Tehran Branch, P.O. Box 11365-4435, Tehran, Iran

    Mansour Sheikhan, Reza Shahnazi & Ali Nooshad Yousefi

Authors
  1. Mansour Sheikhan
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  2. Reza Shahnazi
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  3. Ali Nooshad Yousefi
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Correspondence to Mansour Sheikhan.

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Sheikhan, M., Shahnazi, R. & Nooshad Yousefi, A. An optimal fuzzy PI controller to capture the maximum power for variable-speed wind turbines. Neural Comput & Applic 23, 1359–1368 (2013). https://doi.org/10.1007/s00521-012-1081-4

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  • DOI: https://doi.org/10.1007/s00521-012-1081-4

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