Floating Offshore Wind Turbines (FOWT) face control challenges due to right-half-plane zeros in the open-loop transfer function between blade pitch and rotor speed in above-rated conditions, which limit controller performance and can lead to closed-loop instability. Detuning the controller is a common workaround which sacrifices performance to ensure stability. Auxiliary feedback loops offer a promising alternative, enabling improved rotor speed regulation without compromising stability. However, while these loops enhance control potential, they also introduce additional dynamic interactions and coupling effects that significantly complicate system behavior. As a result, careful attention must be paid to gain scheduling to avoid adverse interactions and ensure robust performance. Existing gain scheduling strategies for multi-loop systems typically rely on robust stability margins, often neglecting performance-oriented design. This work proposes a novel gain scheduling method based on a linear quadratic regulator that enhances power tracking performance without modifying the existing Reference Open Source COntroller (ROSCO) architecture.

This study investigates the capability of FAST.Farm, a mid-fidelity wind farm simulation tool employing the dynamic wake meandering approach, to accurately predict loads on wind turbines in a small wind farm. The wind farm consists of three 1:150 scale models of the DTU 10 MW wind turbine tested in a wind tunnel under scenarios including steady-state operation, wake steering, and dynamic wake actuation. The results demonstrate that FAST.Farm, once calibrated with experimental data, effectively predicts the thrust force and yaw moment of wind turbines across diverse wake conditions. Notably, the Curl wake model—designed to replicate the kidney-shaped wake deficit—has better accuracy in capturing yaw moments of downstream turbines under yaw misalignment. However, its tendency to overestimate wake expansion reduces accuracy in non-skewed inflow scenarios compared to the Polar model. The study highlights the necessity of optimizing FAST.Farm dynamic wake meandering parameters to enhance its precision, particularly by accounting for turbine spacing and wake interactions. Furthermore, it is crucial to improve the accuracy of aerodynamic load calculations under skewed inflow conditions. These findings provide a validated framework for advancing wind farm simulation tools and optimizing wind turbine performance in complex operational conditions.