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A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm


  • Assareh, Ehsanolah
  • Biglari, Mojtaba


This paper presents a hybrid method for generator torque control in wind turbines. The generator torque control is usually used in lower wind speeds in order to capture the maximum power. In the proposed method, the wind turbine generator torque is regulated using a proportional and integral (PI) controller. In order to tune the PI gains, a radial basis function (RBF) neural network is used. The optimal dataset to train this neural network is provided by the Gravitational Search Algorithm (GSA). A 5MW wind turbine model based on FAST (Fatigue, Aero-dynamics, Structures and Turbulence) software code developed at the US National Renewable Energy Laboratory (NREL) is used to simulate and verify the results. The simulation results show that the proposed method has a good performance.

Suggested Citation

  • Assareh, Ehsanolah & Biglari, Mojtaba, 2015. "A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1023-1037.
  • Handle: RePEc:eee:rensus:v:51:y:2015:i:c:p:1023-1037
    DOI: 10.1016/j.rser.2015.07.034

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    References listed on IDEAS

    1. Mérida, Jován & Aguilar, Luis T. & Dávila, Jorge, 2014. "Analysis and synthesis of sliding mode control for large scale variable speed wind turbine for power optimization," Renewable Energy, Elsevier, vol. 71(C), pages 715-728.
    2. Jena, Debashisha & Rajendran, Saravanakumar, 2015. "A review of estimation of effective wind speed based control of wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 1046-1062.
    3. Belmokhtar, K. & Doumbia, M.L. & Agbossou, K., 2014. "Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator)," Energy, Elsevier, vol. 76(C), pages 679-693.
    4. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    5. Oh, Ki-Yong & Park, Joon-Young & Lee, Jun-Shin & Lee, JaeKyung, 2015. "Implementation of a torque and a collective pitch controller in a wind turbine simulator to characterize the dynamics at three control regions," Renewable Energy, Elsevier, vol. 79(C), pages 150-160.
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    7. Mostafaeipour, Ali, 2010. "Feasibility study of offshore wind turbine installation in Iran compared with the world," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 1722-1743, September.
    8. Kortabarria, Iñigo & Andreu, Jon & Martínez de Alegría, Iñigo & Jiménez, Jaime & Gárate, José Ignacio & Robles, Eider, 2014. "A novel adaptative maximum power point tracking algorithm for small wind turbines," Renewable Energy, Elsevier, vol. 63(C), pages 785-796.
    9. Chehouri, Adam & Younes, Rafic & Ilinca, Adrian & Perron, Jean, 2015. "Review of performance optimization techniques applied to wind turbines," Applied Energy, Elsevier, vol. 142(C), pages 361-388.
    10. Mostafaeipour, Ali, 2010. "Feasibility study of harnessing wind energy for turbine installation in province of Yazd in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 93-111, January.
    11. Hassan, H.M. & ElShafei, A.L. & Farag, W.A. & Saad, M.S., 2012. "A robust LMI-based pitch controller for large wind turbines," Renewable Energy, Elsevier, vol. 44(C), pages 63-71.
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    Cited by:

    1. Tiwari, Ramji & Babu, N. Ramesh, 2016. "Recent developments of control strategies for wind energy conversion system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 268-285.
    2. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
    3. repec:eee:appene:v:228:y:2018:i:c:p:1822-1836 is not listed on IDEAS


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