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– (Formula presented.) at (Formula presented.), (Formula presented.) – (Formula presented.) at (Formula presented.), and (Formula presented.) – (Formula presented.) at (Formula presented.), with all paired (Formula presented.) 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 (Formula presented.) – (Formula presented.), whereas the tower-base DEL increases by (Formula presented.) – (Formula presented.), and the FairTen1 response generally increases by (Formula presented.) – (Formula presented.). The farm-mean yaw-bearing response is mixed, ranging from a (Formula presented.) reduction to a (Formula presented.) increase. Turbine-level analysis reveals larger localized penalties, reaching approximately (Formula presented.) for the yaw-bearing DEL and (Formula presented.) 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– (Formula presented.) interval produces an annual energy-contribution increase of (Formula presented.), or (Formula presented.). 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.

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

Ebrahimi, Majid;Bellini, Federico;Fontanella, Alessandro;Muggiasca, Sara;Belloli, Marco
2026-01-01

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– (Formula presented.) at (Formula presented.), (Formula presented.) – (Formula presented.) at (Formula presented.), and (Formula presented.) – (Formula presented.) at (Formula presented.), with all paired (Formula presented.) 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 (Formula presented.) – (Formula presented.), whereas the tower-base DEL increases by (Formula presented.) – (Formula presented.), and the FairTen1 response generally increases by (Formula presented.) – (Formula presented.). The farm-mean yaw-bearing response is mixed, ranging from a (Formula presented.) reduction to a (Formula presented.) increase. Turbine-level analysis reveals larger localized penalties, reaching approximately (Formula presented.) for the yaw-bearing DEL and (Formula presented.) 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– (Formula presented.) interval produces an annual energy-contribution increase of (Formula presented.), or (Formula presented.). 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.
2026
dynamic wake meandering; FAST.Farm; floating offshore wind farm; FLORIS; ROSCO; wake steering; ZeroMQ;
dynamic wake meandering; FAST.Farm; floating offshore wind farm; FLORIS; ROSCO; wake steering; ZeroMQ
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1324045
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