To meet global climate goals, the deployment of renewable energy sources such as wind energy must increase significantly. As wind farms grow in size and density, wake interactions between turbines limit overall performance. Wind Farm Flow Control (WFFC) addresses this challenge with the aim of improving overall wind farm efficiency. Wake steering is a promising control strategy, in which upstream turbines are intentionally misaligned with the wind direction to redirect their wakes away from downstream turbines to increase power production.
Most wake-steering studies rely on steady-state models and focus primarily on power maximization. However, reducing the cost of wind energy also requires extending turbine lifetime, since downstream turbines operating in wake-induced flow experience increased fatigue loading. Aeroelastic simulators can capture these load dynamics, but they are too computationally expensive for control-oriented applications. An efficient alternative is to use load surrogate models that predict fatigue loads from simple inflow and operational quantities. At the same time, the transition from steady-state flow models to dynamic flow models enables the representation of time-varying wake evolution and provides a more realistic environment for evaluating control strategies.
In this thesis, the dynamic wind farm model OFF is coupled with a sector-averaged load surrogate model that predicts damage equivalent loads (DELs) from turbine operation conditions and simple inflow quantities sampled across the rotor plane. The implementation process is described in detail, together with the challenges and limitations encountered. This integrated framework is then used to develop a power-load balanced control strategy that incorporates tower base fatigue loads, and is applied in both the steady state model FLORIS and OFF to assess its performance under more realistic conditions.
A case study with three turbines and three time-varying wind direction signals shows that the resulting balanced controller in OFF achieves power gains of up to 12.57% and tower base load reductions of up to 14.40% compared to the baseline. A comparison between FLORIS and OFF reveals that FLORIS provides an upper bound on predicted power gains and load reductions. The structural differences between steady-state and dynamic models explain the observed differences in predicted power and load responses.