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Optimal Wind Turbine Operation by Artificial Neural Network-Based Active Gurney Flap Flow Control

Author

Listed:
  • Aitor Saenz-Aguirre

    (Automatic Control and System Engineering Department, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Unai Fernandez-Gamiz

    (Nuclear Engineering and Fluid Mechanics Department, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Ekaitz Zulueta

    (Automatic Control and System Engineering Department, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Alain Ulazia

    (Nuclear Engineering and Fluid Mechanics Department, University of the Basque Country (UPV/EHU), Avenida Otaola, 29, 20600 Eibar, Spain)

  • Jon Martinez-Rico

    (IK4-Tekniker, Parke Teknologikoa, Iñaki Goenaga, 5, 20600 Eibar, Spain)

Abstract

Flow control devices have been introduced in the wind energy sector to improve the aerodynamic behavior of the wind turbine blades (WTBs). Among these flow control devices, Gurney flaps (GFs) have been the focus of innovative research, due to their good characteristics which enhance the lift force that causes the rotation of the wind turbine rotor. The lift force increment introduced by GFs depends on the physical characteristics of the device and the angle of attack (AoA) of the incoming wind. Hence, despite a careful and detailed design, the real performance of the GFs is conditioned by an external factor, the wind. In this paper, an active operation of GFs is proposed in order to optimize their performance. The objective of the active Gurney flap (AGF) flow control technique is to enhance the aerodynamic adaption capability of the wind turbine and, thus, achieve an optimal operation in response to fast variations in the incoming wind. In order to facilitate the management of the information used by the AGF strategy, the aerodynamic data calculated by computational fluid dynamics (CFD) are stored in an artificial neural network (ANN). Blade element momentum (BEM) based calculations have been performed to analyze the aerodynamic behavior of the WTBs with the proposed AGF strategy and calculate the corresponding operation of the wind turbine. Real wind speed values from a meteorological station in Salt Lake City, Utah, USA, have been used for the steady BEM calculations. The obtained results show a considerable improvement in the performance of the wind turbine, in the form of an enhanced generated energy output value and a reduced bending moment at the root of the WTB.

Suggested Citation

  • Aitor Saenz-Aguirre & Unai Fernandez-Gamiz & Ekaitz Zulueta & Alain Ulazia & Jon Martinez-Rico, 2019. "Optimal Wind Turbine Operation by Artificial Neural Network-Based Active Gurney Flap Flow Control," Sustainability, MDPI, vol. 11(10), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2809-:d:231746
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    References listed on IDEAS

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    Cited by:

    1. Alejandro Ballesteros-Coll & Unai Fernandez-Gamiz & Iñigo Aramendia & Ekaitz Zulueta & José Antonio Ramos-Hernanz, 2020. "Cell-Set Modelling for a Microtab Implementation on a DU91W(2)250 Airfoil," Energies, MDPI, vol. 13(24), pages 1-15, December.
    2. Md Zishan Akhter & Farag Khalifa Omar, 2021. "Review of Flow-Control Devices for Wind-Turbine Performance Enhancement," Energies, MDPI, vol. 14(5), pages 1-35, February.
    3. Piotr Wiśniewski & Francesco Balduzzi & Zbigniew Buliński & Alessandro Bianchini, 2020. "Numerical Analysis on the Effectiveness of Gurney Flaps as Power Augmentation Devices for Airfoils Subject to a Continuous Variation of the Angle of Attack by Use of Full and Surrogate Models," Energies, MDPI, vol. 13(8), pages 1-25, April.
    4. 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.
    5. Fang, Jianhao & Hu, Weifei & Liu, Zhenyu & Chen, Weiyi & Tan, Jianrong & Jiang, Zhiyu & Verma, Amrit Shankar, 2022. "Wind turbine rotor speed design optimization considering rain erosion based on deep reinforcement learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    6. Elsayed, Ahmed M. & Khalifa, Mohamed A. & Benini, Ernesto & Aziz, Mohamed A., 2023. "Experimental and numerical investigations of aerodynamic characteristics for wind turbine airfoil using multi-suction jets," Energy, Elsevier, vol. 275(C).

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