| 18 Aug 2026
Static Closed-Loop Wake Steering and Induction Control in Floating Wind Farms for Load-Constrained Power Optimization
Abstract. This work develops and assesses static, closed-loop wind-farm flow-control strategies for load-constrained power optimization in a floating wind farm. Three farm-level strategies are considered: wake steering using turbine-specific nacelle-heading offsets, pitch-based induction control using discrete derating levels, and a hybrid yaw–induction strategy. A supervisory FLORIS engineering wake model computes the control setpoints by maximizing the predicted farm power. For induction and hybrid control, the power optimization is subject to a farm-level rotor-thrust constraint, which is used as an aerodynamic load-related proxy. The optimized setpoints are applied to a coupled FAST.Farm aero-hydro-servo-elastic model through independent ROSCO turbine controllers and a ZeroMQ communication interface. The resulting farm power, rotor thrust, structural loads, mooring loads, and spectral responses are then evaluated from the FAST.Farm simulations.
The investigated farm consists of four IEA 15 MW reference wind turbines mounted on UMaine VolturnUS-S semi-submersible platforms. The strategies are tested under below-rated turbulent wind conditions and compared against greedy operation, in which each turbine independently follows its maximum-power-point control strategy. The FLORIS model is calibrated against FAST.Farm response data using turbine power and thrust lookup tables for the considered derating levels together with wake-model parameter tuning.
The results show that all three strategies increase mean farm power relative to greedy operation, but with distinct power–load trade-offs. Within the investigated simulation matrix, hybrid control provides FAST.Farm-predicted farm-power gains ranging from 8.7 % to 19 %. Wake steering generally increases yaw-bearing damage-equivalent loads, whereas induction and hybrid control reduce yaw-bearing DELs and, to a lesser extent, blade-root moment DELs for most investigated conditions. Tower-base moment and fairlead-tension responses are less systematic and depend strongly on wind direction and coupled floating-platform–mooring dynamics. For the representative U = 9 m s-1, WD = -5° condition, spectral analysis shows that the controller-induced changes are concentrated in the rotor-harmonic and structural-frequency ranges and in the low-frequency platform–mooring region. Overall, the results demonstrate that load-constrained farm-power optimization can provide substantial power gains in floating wind farms, but that an aggregate rotor-thrust constraint alone does not prevent operating-condition-dependent structural and mooring-load penalties.
I congratulate the authors on this insightful LES study using global POD to characterise surge‑ and pitch‑driven wake dynamics of floating offshore wind turbines. The key finding regarding the role of reduced frequency on coherent wake evolution is valuable for reduced‑order‑model development.
I would encourage the authors to discuss the applicability of their conclusions across different platform motion types. As shown by Messmer et al. (2024), streamwise and lateral motions trigger distinct wake instabilities with different sensitive reduced‑frequency ranges. Since this work only considers surge and pitch, a note on how results may transfer to lateral sway motion would help constrain the generality of their observations for full six‑DOF platform dynamics.
I also suggest considering the resolvent‑based motion‑to‑wake model from Li and Yang (2024, JFM 980, A48). This physics‑driven model predicts motion‑induced coherent wake structures, offering an interesting benchmark for the data‑driven global‑POD results presented here.