The combined wind speed estimator and tip-speed ratio (WSE-TSR) tracking wind turbine control scheme has seen recent and increased traction from the wind industry. The modern control scheme provides a flexible trade-off between power and load objectives. In academia, the Kω2 controller is often used based on its simplicity and steady-state optimality and is taken as a baseline here. This paper demonstrates the steady-state equivalence and dynamic differences between these controllers and presents a systematic procedure for their optimal calibration. For calibration of the control schemes, a multi-objective optimisation problem is formulated with the conflicting objectives of power maximisation and torque fluctuations minimisation. The optimisation problem is solved by approximating the Pareto front based on the set of optimal solutions found by an explorative search. The Pareto fronts obtained for calibration of the baseline and for increasing fidelities of the WSE-TSR tracking controller show that no optimal solution exists, translating into increased power capture with respect to the baseline Kω2 controller. The frequency-domain analysis, however, shows increased control bandwidth for tip-speed ratio reference tracking for the solution leading to power maximisation. If the objective is to reduce the torque variance, the controller bandwidth decreases with a mild penalty on the energy yield. High-fidelity simulations confirm this trend, proving that, if properly calibrated, the WSE-TSR tracking controller obtains approximately the same generated power of the baseline while reducing torque actuation effort.

Economic model predictive control (EMPC) has received increasing attention in the wind energy community due to its ability to trade off economic objectives with ease. However, for wind turbine applications, inherent nonlinearities, such as from aerodynamics, pose difficulties in attaining a convex optimal control problem (OCP), by which real-time deployment is not only possible but also a globally optimal solution is guaranteed. A variable transformation can be utilized to obtain a convex OCP, where nominal variables, such as rotational speed, pitch angle, and torque, are exchanged with an alternative set in terms of power and energy. The ensuing convex EMPC (CEMPC) possesses linear dynamics, convex constraints, and concave economic objectives and has been successfully employed to address power control and tower fatigue alleviation. This work focuses on extending the blade loads mitigation aspect of the CEMPC framework by exploiting its individual pitch control (IPC) capabilities, resulting in a novel CEMPC-IPC technique. This extension is made possible by reformulating static blade and rotor moments in terms of individual blade aerodynamic powers and rotational kinetic energy of the drivetrain. The effectiveness of the proposed method is showcased in a mid-fidelity wind turbine simulation environment in various wind cases, in which comparisons with a basic CEMPC without load mitigation capability and a baseline IPC are made. Results indicate that CEMPC-IPC can achieve better reduction in rotating blade loads, as well as similar performance in the mitigation of shaft and yaw bearing loads, with the added advantage of convenient economic objectives trade-off tuning.

High-fidelity numerical simulations of wind turbines are often performed through a computational fluid dynamics (CFD) model in which the wind turbine is parametrized using an actuator line model, as fully blade-resolved simulations are computationally expensive at the large Reynolds numbers involved. When applying such an actuator line approach to the modeling of wind turbines, the two main issues to consider are the free-stream velocity sampling and the choice of the regularization kernel used to project the computed body forces onto the domain. In this work, a novel velocity sampling method - the so-called effective velocity model (EVM) - is implemented in the CFD software SOWFA and assessed against pre-existing approaches. Results show superior method robustness with respect to the regularization kernel width choice while preserving acceptable accuracy.

Wind farm control can play a key role in reducing the negative impact of wakes on wind turbine power production. The helix approach is a recent innovation in the field of wind farm control, which employs individual blade pitch control to induce a helical velocity profile in a wind turbine wake. This forced meandering of the wake has turned out to be very effective for the recovery of the wake, increasing the power output of downstream turbines by a significant amount. This paper presents a wind tunnel study with two scaled wind turbine models, of which the upstream turbine is operated with the helix approach. We used tomographic particle image velocimetry to study the dynamic behavior of the wake under influence of the helix excitation. The measured flow fields confirm the wake recovery capabilities of the helix approach compared to normal operation. Additional emphasis is put on the effect of the helix approach on the breakdown of blade tip vortices, a process that plays an important role in re-energizing the wake. Measurements indicate that the breakdown of tip vortices, and the resulting destabilization of the wake is enhanced significantly with the helix approach. Finally, turbine measurements show that the helix approach was able to increase the combined power for this particular two turbine setup by as much as 15%.