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Published November 2020 | Submitted + Published
Journal Article Open

Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment


The intentional yaw misalignment of leading, upwind turbines in a wind farm, termed wake steering, has demonstrated potential as a collective control approach for wind farm power maximization. The optimal control strategy and the resulting effect of wake steering on wind farm power production are in part dictated by the power degradation of the upwind yaw misaligned wind turbines. In the atmospheric boundary layer, the wind speed and direction may vary significantly over the wind turbine rotor area, depending on atmospheric conditions and stability, resulting in freestream turbine power production which is asymmetric as a function of the direction of yaw misalignment and which varies during the diurnal cycle. In this study, we propose a model for the power production of a wind turbine in yaw misalignment based on aerodynamic blade elements, which incorporates the effects of wind speed and direction changes over the turbine rotor area in yaw misalignment. The proposed model can be used for the modeling of the angular velocity, aerodynamic torque, and power production of an arbitrary yaw misaligned wind turbine based on the incident velocity profile, wind turbine aerodynamic properties, and turbine control system. A field experiment is performed using multiple utility-scale wind turbines to characterize the power production of yawed freestream operating turbines depending on the wind conditions, and the model is validated using the experimental data. The resulting power production of a yaw misaligned variable speed wind turbine depends on a nonlinear interaction between the yaw misalignment, the atmospheric conditions, and the wind turbine control system.

Additional Information

© 2020 the Author(s). Published under license by AIP Publishing. Submitted: 1 August 2020 . Accepted: 19 November 2020 . Published Online: 21 December 2020. This paper is part of the special issue on Advances in Wind Plant Controls: Strategies, Implementation, and Validation. M.F.H. is funded through a National Science Foundation Graduate Research Fellowship under Grant No. DGE-1656518 and a Stanford Graduate Fellowship. The authors would like to thank Sanjiva Lele for thoughtful suggestions on the blade element model and Varun Sivaram for support throughout this study. DATA AVAILABILITY. The data used in this study are confidential at the request of the wind farm operator.

Attached Files

Published - 5.0023746.pdf

Submitted - 2008.00873.pdf


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