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Power Production and Blade Fatigue of a Wind Turbine Array Subjected to Active Yaw Control

Author

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  • Mou Lin

    (Wind Engineering and Renewable Energy Laboratory (WiRE), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-ENAC-IIE-WIRE, 1015 Lausanne, Switzerland)

  • Fernando Porté-Agel

    (Wind Engineering and Renewable Energy Laboratory (WiRE), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-ENAC-IIE-WIRE, 1015 Lausanne, Switzerland)

Abstract

This study investigated the power production and blade fatigue of a three-turbine array subjected to active yaw control (AYC) in full-wake and partial-wake configurations. A framework of a two-way coupled large eddy simulation (LES) and an aeroelastic blade simulation was applied to simulate the atmospheric boundary layer (ABL) flow through the turbines and the structural responses of the blades. The mean power outputs and blade fatigue loads were extracted from the simulation results. By exploring the feasible AYC decision space, we found that in the full-wake configuration, the local power-optimal AYC strategy with positive yaw angles endures less flapwise blade fatigue and more edgewise blade fatigue than the global power-optimal strategy. In the partial-wake configuration, applying positive AYC in certain inflow wind directions achieves higher optimal power gains than that in the full-wake scenario and reduces blade fatigue from the non-yawed benchmark. Using the blade element momentum (BEM) theory, we reveal that the aforementioned differences in flapwise blade fatigue are due to the differences in the azimuthal distributions of the local relative velocity on blade sections, resulting from the vertical wind shear and blade rotation. Furthermore, the difference in the blade force between the positively and negatively yawed front-row turbine induces different wake velocities and turbulence distributions, causing different fatigue loads on the downwind turbine exposed to the wake.

Suggested Citation

  • Mou Lin & Fernando Porté-Agel, 2023. "Power Production and Blade Fatigue of a Wind Turbine Array Subjected to Active Yaw Control," Energies, MDPI, vol. 16(6), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2542-:d:1090848
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    References listed on IDEAS

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    1. Meng, Hang & Lien, Fue-Sang & Li, Li, 2018. "Elastic actuator line modelling for wake-induced fatigue analysis of horizontal axis wind turbine blade," Renewable Energy, Elsevier, vol. 116(PA), pages 423-437.
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    4. Mou Lin & Fernando Porté-Agel, 2019. "Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models," Energies, MDPI, vol. 12(23), pages 1-18, November.
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    7. Ma, Hongliang & Ge, Mingwei & Wu, Guangxing & Du, Bowen & Liu, Yongqian, 2021. "Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production," Applied Energy, Elsevier, vol. 303(C).
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    Cited by:

    1. Esmail Mahmoodi & Mohammad Khezri & Arash Ebrahimi & Uwe Ritschel & Majid Kamandi, 2024. "A LiDAR-Based Active Yaw Control Strategy for Optimal Wake Steering in Paired Wind Turbines," Energies, MDPI, vol. 17(22), pages 1-14, November.
    2. Alina Fazylova & Baurzhan Tultayev & Teodor Iliev & Ivaylo Stoyanov & Ivan Beloev, 2023. "Development of a Control Unit for the Angle of Attack of a Vertically Axial Wind Turbine," Energies, MDPI, vol. 16(13), pages 1-20, July.
    3. Zhiwen Deng & Chang Xu & Zhihong Huo & Xingxing Han & Feifei Xue, 2023. "Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model," Energies, MDPI, vol. 16(9), pages 1-20, May.

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