Wind turbines operating within a wind farm experience significant power losses due to wake interactions. Dynamic induction control has been proposed as a solution to mitigate these losses by enhancing wake mixing with the free stream, thereby increasing the effective wind speed at downstream rotor planes. Such wind farm flow control strategies typically rely on periodic inputs, characterised by a mean value, amplitude, and frequency. The selection and optimisation of these parameters is, however, commonly performed empirically, due to the high computational cost of high-fidelity flow simulations. Model-free controllers may have the potential to identify a periodic control input tailored to the environmental conditions without relying on a costly flow model. This paper proposes an economic data-driven controller with the objective of wind farm power maximisation. Results are illustrated using a setup of two wind turbines interacting through a free-vortex wake engineering model. The proposed method converges to a periodic input that leads to a total power increase of 6.4% compared to a greedy control scenario. These findings conceptually demonstrate the potential of data-driven methods for dynamic wind farm flow control as a viable and computationally efficient alternative to model-based optimisation. ... Read more

Floating offshore wind turbines pave the way to accessing deep-water regions with abundant wind resources. However, they face specific control challenges, such as the negative damping problem and increased model complexity. Since model-based control is becoming increasingly demanding, a model-free, data-driven approach is considered. Additionally, floating wind turbines are susceptible to rough environmental disturbances. Feedforward information, such as wave elevation measurements from wave radars, may be included in the controller to lessen the impact of disturbances. Although waves have been shown to increase rotor speed oscillations and turbine loads, wave-preview-based methods have only recently been explored. To this end, this paper first proposes a modified Data-enabled Predictive Control formulation that includes past and future information about measurable disturbances. The feasibility of this control strategy is then demonstrated for floating wind turbines through mid-fidelity simulations. The model-free, feedforward controller uses a preview of wave forces acting on the floating platform and aims for rotor speed regulation. Simulations indicate that the data-driven approach has potential for floating wind turbine control, and including wave feedforward action reduces the amplitude of rotor speed oscillations. ... Read more