Maximising the power production of wind farms is vital to meet the growing demand for wind
energy and reduce its cost. Wake effects, resulting from the aerodynamic interactions between tur‐
bines in a wind farm, significantly impact farm efficiency, leading to substantial annual power losses.
Wake steering, an influential control strategy, involves mitigating wake effects by strategically yaw
misaligning upstream turbines to deflect their wakes. Conventional wake steering approaches typically rely on physics‐based analytical wake models with their parameters often calibrated using
higher‐fidelity data. However, these approaches determine a fixed set of parameters prior to con‐
ducting wake steering, neglecting each parameter’s dependency on yaw misalignment (i.e. the
optimisation variables) exhibited throughout the optimisation process, potentially affecting its accuracy. To address this limitation, this paper introduces a novel data‐driven parameter tuning approach
which integrates higher‐fidelity power measurements using Gaussian Processes to continuously
adapt parameters in lower‐fidelity wake models based on the current farm’s yaw configuration.
The effectiveness of the proposed approach is demonstrated on a 5 × 5 wind farm and a layout
corresponding to the Horns Rev wind farm, where various wind directions are investigated. The results reveal that the approach can enable a lower‐fidelity model to capture more complex physics,
thereby improving its accuracy in wake steering optimisation, while maintaining robustness and
computational efficiency. This method holds promise for real‐time control applications and can be
extended to other control strategies and closed‐loop frameworks.