Simulation methods ensuring a level of fidelity higher than that of the ubiquitous Blade Element Momentum theory are increasingly applied to VAWTs, ranging from Lifting-Line methods, to Actuator Line or Computational Fluid Dynamics (CFD). The inherent complexity of these machines, characterised by a continuous variation of the angle of attack during the cycloidal motion of the airfoils and the onset of many related unsteady phenomena, makes nonetheless a correct estimation of the actual aerodynamics extremely difficult. In particular, a better understanding of the actual angle of attack during the motion of a VAWT is pivotal to select the correct airfoil and functioning design conditions. Moving from this background, a high-fidelity unsteady CFD model of a 2-blade H-Darrieus rotor was developed and validated against unique experimental data collected using Particle Image Velocimetry (PIV). In order to reconstruct the AoA variation during one rotor revolution, three different methods-detailed in the study-were then applied to the computed CFD flow fields. The resulting AoA trends were combined with available blade forces data to assess the corresponding lift and drag coefficients over one rotor revolution and correlate them with the most evident flow macro-structures and with the onset of dynamic stall.

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The paper presents an experimental study of applying variable loads on a vertical-axis wind turbine (VAWT). The experiment is conducted in an open-jet wind tunnel on a two-bladed Darrieus VAWT equipped with active individual blade pitch control. Variable loads are achieved by dynamically changing the pitch angle of the individual blades and by keeping the wind speed of the tunnel constant. The blade loads are measured using strain gages and the flow velocity is measured upwind and downwind of the rotor using a hotwire. Dynamic inflow phenomena are clearly visible both in the turbine loads and in the velocity field. A time delay based upon the flow convection in the wake is identified. It results that the induction of the turbine can be controlled by changing the pitch of the blades. The experimental database allows to validate a new dynamic inflow model for VAWT and will be made publicly available for research purposes.

Abstract. 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. On the other hand, the Kω2 controller is often used based on its simplicity and steady-state optimality and is taken as a baseline here. This paper investigates the potential benefits of the WSE-TSR tracking controller compared to the baseline by analysis through a frequency-domain framework and by optimal calibration through a systematic procedure. A multi-objective optimisation problem is formulated for calibration 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 on the NREL 5MW reference turbine 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.

Wind turbine partial-load controllers have evolved from simple static nonlinear function implementations to more advanced dynamic controller structures. Such dynamic control schemes have the potential to improve power production performance in realistic environmental conditions and allow for a more granular trade-off between loads and energy capture. The control structure generally consists of a wind speed estimator (WSE) combined with a controller aiming to track the commanded tip-speed ratio (TSR) reference. The performance and resulting closed-loop system stability are however highly dependent on the accuracy of the internal model in the WSE-TSR tracking scheme. Therefore, developing learning algorithms to calibrate the internal model is of particular interest. Previous works have proposed such algorithms; however, they all rely on the availability of (rotor-effective) wind speed measurements. For the first time, this paper proposes an excitation-based learning algorithm that exploits the closedloop dynamic structure of the WSE-TSR tracking scheme. This algorithm calibrates the internal model without the need for wind speed measurements. Analysis and simulations show that the proposed algorithm corrects for model uncertainties in the form of magnitude scaling errors under ideal constant and realistic turbulent wind conditions.