LAPSE:2023.31079
Published Article

LAPSE:2023.31079
Power Production and Blade Fatigue of a Wind Turbine Array Subjected to Active Yaw Control
April 18, 2023
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.
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.
Record ID
Keywords
active yaw control, wind power, wind turbine fatigue
Subject
Suggested Citation
Lin M, Porté-Agel F. Power Production and Blade Fatigue of a Wind Turbine Array Subjected to Active Yaw Control. (2023). LAPSE:2023.31079
Author Affiliations
Lin M: Wind Engineering and Renewable Energy Laboratory (WiRE), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-ENAC-IIE-WIRE, 1015 Lausanne, Switzerland
Porté-Agel F: Wind Engineering and Renewable Energy Laboratory (WiRE), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-ENAC-IIE-WIRE, 1015 Lausanne, Switzerland [ORCID]
Porté-Agel F: Wind Engineering and Renewable Energy Laboratory (WiRE), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-ENAC-IIE-WIRE, 1015 Lausanne, Switzerland [ORCID]
Journal Name
Energies
Volume
16
Issue
6
First Page
2542
Year
2023
Publication Date
2023-03-08
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en16062542, Publication Type: Journal Article
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LAPSE:2023.31079
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https://doi.org/10.3390/en16062542
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Apr 18, 2023
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