Power and Fatigue–Load Assessment of Static Wake Steering in a Floating Wind Farm with 15 MW Turbines

Abstract

Static wake steering can increase wind-farm power production, but its application to floating offshore wind farms requires assessment of the coupled wake, platform, structural, and station-keeping response. This study evaluates whether power-maximizing static yaw setpoints identified using the steady, control-oriented FLORIS model retain their benefit when transferred without re-optimization to a coupled FAST.Farm floating wind-farm model. The reference farm comprises four IEA Wind 15 MW turbines mounted on VolturnUS-S semi-submersible platforms. Greedy and static wake-steering operations are compared at three below-rated wind speeds, three sea states, and five matched turbulent-inflow realizations, resulting in 90 farm-level FAST.Farm simulations. Wake behavior is characterized through wake-center deflection, meandering, and velocity-deficit profiles, while turbine and mooring fatigue responses are evaluated using paired damage-equivalent-load statistics. Static wake steering increases mean farm power under all nine investigated wind–wave conditions. The gains are approximately 5.1–5.2% at 7ms−1, 5.0–5.1% at 8ms−1, and 4.0–4.2% at 9ms−1, with all paired 95% confidence intervals remaining above zero. The gain results from a power redistribution in which the intentionally yawed upstream turbine incurs a local loss that is exceeded by the combined recovery of the downstream turbines. The fatigue response is strongly component- and turbine-dependent. The paired farm-mean blade-root DEL decreases by 0.82–2.24%, whereas the tower-base DEL increases by 0.76–2.78%, and the FairTen1 response generally increases by 0.88–2.92%. The farm-mean yaw-bearing response is mixed, ranging from a 1.15% reduction to a 4.32% increase. Turbine-level analysis reveals larger localized penalties, reaching approximately 10.4% for the yaw-bearing DEL and 12.8% for FairTen1. Spectral analysis associates the yaw-bearing response with yaw-induced aerodynamic and structural excitation, while the tower-base response is strongly influenced by low-frequency wave–platform dynamics. A complementary FLORIS sensitivity analysis demonstrates that the optimized aerodynamic benefit depends strongly on wind direction, spacing, wind speed, and turbulence intensity. For a Tampen-derived 11-turbine layout, resource weighting over the modeled 4–13ms−1 interval produces an annual energy-contribution increase of 3.653GWhyear−1, or 0.921%. These results provide numerical evidence that static wake steering can retain a positive power benefit in a coupled floating wind-farm environment, but controller assessment must include turbine- and component-specific dynamic loads rather than farm power alone.