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Wind-turbine collective-pitch control via a fuzzy predictive algorithm

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  • Lasheen, Ahmed
  • Elshafei, Abdel Latif

Abstract

This paper proposes a new fuzzy predictive algorithm for collective pitch control of large wind turbines. Collective pitch controllers operate in region three to harvest the rated power and maintain the rated speed. The wind turbine model is represented by a Takagi–Sugeno (T–S) fuzzy model. The number of T–S fuzzy rules is reduced based on a gap – metric criterion. A model predictive controller is designed based on the fuzzy model taking into consideration the pitch actuator constraints. The proposed controller is coupled with conventional PI controllers for individual pitch control so as to minimize the moments on the turbine blades. A Kalman observer is designed to estimate the immeasurable states. The performance of the proposed fuzzy-predictive controller is compared to a gain schedule PI controller and a mixed H2/H∞ controller. Simulation results, based on a typical 5-MW offshore wind turbine, demonstrate the superiority of the proposed fuzzy-predictive controller.

Suggested Citation

  • Lasheen, Ahmed & Elshafei, Abdel Latif, 2016. "Wind-turbine collective-pitch control via a fuzzy predictive algorithm," Renewable Energy, Elsevier, vol. 87(P1), pages 298-306.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p1:p:298-306
    DOI: 10.1016/j.renene.2015.10.030
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    References listed on IDEAS

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    1. Bottasso, C.L. & Croce, A. & Riboldi, C.E.D. & Nam, Y., 2013. "Multi-layer control architecture for the reduction of deterministic and non-deterministic loads on wind turbines," Renewable Energy, Elsevier, vol. 51(C), pages 159-169.
    2. 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:

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    2. Lasheen, Ahmed & Saad, Mohamed S. & Emara, Hassan M. & Elshafei, Abdel Latif, 2019. "Tube-based explicit model predictive output-feedback controller for collective pitching of wind turbines," Renewable Energy, Elsevier, vol. 131(C), pages 549-562.
    3. Alhassan H. Alattar & S. I. Selem & Hamid M. B. Metwally & Ahmed Ibrahim & Raef Aboelsaud & Mohamed A. Tolba & Ali M. El-Rifaie, 2019. "Performance Enhancement of Micro Grid System with SMES Storage System Based on Mine Blast Optimization Algorithm," Energies, MDPI, vol. 12(16), pages 1-23, August.
    4. Li, Jianshen & Wang, Shuangxin & Li, Yaguang, 2020. "A model-free adaptive controller with tracking error differential for collective pitching of wind turbines," Renewable Energy, Elsevier, vol. 161(C), pages 435-447.
    5. 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.
    6. Hawari, Qusay & Kim, Taeseong & Ward, Christopher & Fleming, James, 2022. "A robust gain scheduling method for a PI collective pitch controller of multi-MW onshore wind turbines," Renewable Energy, Elsevier, vol. 192(C), pages 443-455.
    7. Lasheen, Ahmed & Saad, Mohamed S. & Emara, Hassan M. & Elshafei, Abdel Latif, 2017. "Continuous-time tube-based explicit model predictive control for collective pitching of wind turbines," Energy, Elsevier, vol. 118(C), pages 1222-1233.
    8. Afef Fekih & Saleh Mobayen & Chih-Chiang Chen, 2021. "Adaptive Robust Fault-Tolerant Control Design for Wind Turbines Subject to Pitch Actuator Faults," Energies, MDPI, vol. 14(6), pages 1-13, March.
    9. Guo, Wencheng & Yang, Jiandong, 2018. "Dynamic performance analysis of hydro-turbine governing system considering combined effect of downstream surge tank and sloping ceiling tailrace tunnel," Renewable Energy, Elsevier, vol. 129(PA), pages 638-651.
    10. Zhicheng Lin & Song Zheng & Zhicheng Chen & Rong Zheng & Wang Zhang, 2019. "Application Research of the Parallel System Theory and the Data Engine Approach in Wind Energy Conversion System," Energies, MDPI, vol. 12(5), pages 1-20, March.
    11. Pablo Zambrana & Javier Fernandez-Quijano & J. Jesus Fernandez-Lozano & Pedro M. Mayorga Rubio & Alfonso J. Garcia-Cerezo, 2021. "Improving the Performance of Controllers for Wind Turbines on Semi-Submersible Offshore Platforms: Fuzzy Supervisor Control," Energies, MDPI, vol. 14(19), pages 1-17, September.
    12. Baiomy, Nehal & Kikuuwe, Ryo, 2020. "An amplitude- and rate-saturated collective pitch controller for wind turbine systems," Renewable Energy, Elsevier, vol. 158(C), pages 400-409.
    13. Azizi, Askar & Nourisola, Hamid & Shoja-Majidabad, Sajjad, 2019. "Fault tolerant control of wind turbines with an adaptive output feedback sliding mode controller," Renewable Energy, Elsevier, vol. 135(C), pages 55-65.
    14. El-Baklish, Shaimaa K. & El-Badawy, Ayman A. & Frison, Gianluca & Diehl, Moritz, 2020. "Nonlinear model predictive pitch control of aero-elastic wind turbine blades," Renewable Energy, Elsevier, vol. 161(C), pages 777-791.
    15. Amira Elkodama & Amr Ismaiel & A. Abdellatif & S. Shaaban & Shigeo Yoshida & Mostafa A. Rushdi, 2023. "Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review," Energies, MDPI, vol. 16(17), pages 1-32, September.
    16. Li, Jianshen & Wang, Shuangxin, 2021. "Dual multivariable model-free adaptive individual pitch control for load reduction in wind turbines with actuator faults," Renewable Energy, Elsevier, vol. 174(C), pages 293-304.

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