Data-Driven Phase Synchronization for Dynamic Repositioning of Floating Wind Turbines

Abstract

Wind energy is an increasingly growing source of energy. To increase the capacity factor of existing wind farms, and to improve the design of future wind farms, it is important to address the wake effect. Wake interactions negatively impact the efficiency of wind farms, and wind farm flow control techniques are developed to mitigate this. Floating wind farms have the unique possibility of repositioning turbines by controlling the aerodynamic thrust force. This enables the adaptation of the wind farm layout to the wind conditions, maximizing the farm power output. The conventional strategy for turbine repositioning is to reposition turbines to a steady-state optimal solution. While initial results on this technique are promising, it is highlighted that mooring lines need to be lengthened substantially to facilitate sufficient displacement. Recent work has shown that for tauter mooring configurations, the optimal repositioning solutions are dynamic, and involve periodic yaw control signals that result in dynamic turbine repositioning. Dynamic turbine repositioning poses a synchronization problem, as the phase difference between the actuation of the turbines is critical to the effectiveness of the technique. Therefore, this work presents a data-driven solution with extremum seeking control, to control the phase difference for dynamic repositioning, and thereby maximize the wind farm power production. The technique is demonstrated in a mid-fidelity simulation environment, and it is shown that dynamic repositioning attains a higher power output and requires a lower yaw amplitude compared to static repositioning for a wind farm consisting of two 15 MW turbines.