In the pursuit of mitigating the wake effect, floating wind turbines have additional degrees of freedom compared to their fixed-bottom counterparts. The mooring system with which floating wind turbines are anchored to the seabed allows a range of motion in which turbines can be repositioned. Turbine repositioning uses yaw control to reposition floating wind turbines, and to thereby actively optimize the wind farm layout. Previous research has focused on obtaining optimal steady-state yaw angles for turbine repositioning by using steady-state wake models. Here, the primary conclusion is that mooring line tension needs to be relaxed to facilitate a range of movement large enough for steady-state turbine repositioning to be effective. The presented work studies the effect of using dynamic yaw signals for turbine repositioning by using a dynamic wake model. To study the effect of including wake dynamics, an optimization problem to find the optimal yaw control signals for a two turbine floating wind farm is solved for various mooring configurations. This work shows that for stiffer mooring configurations, turbine repositioning can still be leveraged to increase wind farm efficiency, but that the optimal yaw control action is dynamic for these cases.

The dynamic induction control wake mixing strategy has the potential to increase the energy yield of floating wind farms. These floating turbines will be subjected to surface waves, caused by the wind, and swell. When dynamic induction control is applied in open-loop, the effect of second-order wave forces and dynamic induction control on the thrust force can be out-of-phase and have destructive interference. In this work, we propose a method to synchronize the dynamic induction control input to the effect of the second-order wave forces. This is achieved by formulating the synchronization problem within an H_∞ optimization framework and designing a controller that minimizes the difference between the effect of wave-induced thrust variation and thrust variation. Time domain simulations show that synchronization at a desired frequency can be achieved and that the overall performance of the dynamic induction control method can be enhanced.

Achieving the European Union's target of 510 GW of installed wind energy capacity by 2030 requires a significant expansion of the currently installed capacity of 255 GW [1], [2]. As a consequence of these ambitions, the power density of newly developed wind farms is rising by increasing the number of turbines within a wind farm and the size of individual turbines [3]. The larger wind farms are predominantly located offshore where wind conditions are more consistent and, on average, wind speeds are higher compared to onshore locations [4]. Furthermore, more than 80% of Europe's wind energy resources can be found in waters too deep for bottom-fixed turbines [5], [6], resulting in a sharp increase in the interest in floating wind turbines over the past decade (see 'Summary').

Wake mixing techniques like the Helix have shown to be effective at reducing the wake interaction between turbines, which improves wind farm power production. When these techniques are applied to a floating turbine it will excite movement. The type and magnitude of movement are dependent on floater dynamics. This work investigates four different floating turbines. Of these four turbines, two are optimised variants of the TripleSpar and Softwind platforms with enhanced yaw motion. The other two are the unaltered versions of these platforms. When the Helix is applied to all four floating turbines, the increased yaw motion of the optimised TripleSpar results in a reduction in windspeed whereas the optimised Softwind sees an increase in windspeed with increased yaw motion. From simulations using prescribed yaw motion at different phase offsets between blade pitch and yaw motion, we can conclude that this is the driving factor for this difference.

Dynamic induction control is a wind farm flow control strategy that utilises wind turbine thrust variations to accelerate breakdown of the aerodynamic wake and improve downstream turbine performance. However, when floating wind turbines are considered, additional dynamics and challenges appear that make optimal control difficult. In this work, we propose an adjoint optimisation framework for non-linear economic model-predictive control, which utilises a novel coupling of an existing aerodynamic wake model to floating platform hydrodynamics. Analysis of the frequency response for the coupled model shows that it is possible to achieve wind turbine thrust variations without inducing large motion of the rotor. Using economic model-predictive control, we find dynamic induction results that lead to an improvement of 7 % over static induction control, where the dynamic controller stimulates wake breakdown with only small variations in rotor displacement. This novel model formulation provides a starting point for the adaptation of dynamic wind farm flow control strategies for floating wind turbines.

In recent years dynamic induction control has shown great potential in reducing wake-to-turbine interaction by increasing the mixing in the wake. With these wake mixing methods the thrust force will vary in time. If applied to a floating offshore wind turbine, it will cause the platform to move. In this paper the effect of the Helix mixing approach on a DTU10MW turbine on the TripleSpar platform and its wake is evaluated. When the Helix mixing approach is applied at Strouhal equal to 0.25, the yaw movement is excited close to the eigenfrequency of the platform resulting in significant yaw angles for small blade pitch angles. To understand the impact of the motion on the wake, the yaw motion is simulated using the free wake vortex method as implemented in Qblade. Under laminar inflow, results show that the windspeed at a distance of 5 rotor diameters downstream can be increased by up to 10% compared to a fixed-bottom turbine.

Lateral control in the absence of lane markings is an essential safety fallback for an autonomous vehicle in cooperative driving applications. Point following control is one such solutions for lateral control. However, it suffers from corner cutting and severe disturbance amplification throughout the platoon. In this paper, a new model for controller synthesis is proposed which supports including the error induced by the road curvature in the communication between two vehicles. This enables the trailing vehicle to deduce the actual road error states, which negates steady-state corner cutting if these errors are controlled to zero. To demonstrate the benefits of this new control model, an control framework is used to design a lateral controller which minimizes the lateral overshoot of the vehicles during transient maneuvering. The proposed approach has been evaluated using numerical simulations. Simulation results show that the lateral overshoot can be reduced by a factor 10 with respect to existing lateral control solutions.