Wind tunnel measurements are performed to investigate the potential for mitigation of aerodynamic load fluctuations on airfoils, as the main source of fatigue for wind turbines, using plasma actuators. The experiment consists of aerodynamic force measurements using six strain gauges and 2-component velocity measurements using particle image velocimetry (PIV). The analysis is focused on the aerodynamic loads, the mean flow, the turbulent kinetic energy and the proper orthogonal decomposition (POD) modes and their energy budget. The main findings are: (i) the actuation increased the lift coefficient for all the range of a, with an average and maximum increment of 0.05 and 0.1, per unit span of the actuator; (ii) the actuation deflects the airfoil near wake downward, resulting in the so-called virtual cambering effect; (iii) the actuation increases the TKE near the trailing edge, which could increase the airfoil trailing-edge noise; (iv) the POD analysis reveals that the actuation increases the size of the vortical structures in the near wake and the energy budget of the first POD modes, esp. at high angles of attack prior to stall.

Unsteady loads are a major limiting factor for further upscaling of HAWTs considering the high costs associated to strict structural requirements. Alleviation of these unsteady loads on HAWT blades, e.g. using active flow control (AFC), is of high importance. In order to devise effective AFC methods, the unsteady loading sources need to be identified and their relative contribution to the load fluctuations experienced by blades needs to be quantified. The current study investigates the effects of various atmospheric and operational parameters on the fluctuations of α and C_L for a large HAWT. The investigated parameters include turbulence, wind shear, yawed inflow, tower shadow, gravity and rotational imbalances. The study uses the DTU's aeroelastic software HAWC2. The study identifies the individual and the aggregate effect of each source on the aforementioned fluctuations in order to distinguish the major contributing factors to unsteady loading. The quantification of contribution of each source on the total fatigue loads reveals >65% of flapwise fatigue loads is a result of turbulence while gravity results in >80% of edgewise fatigue loads. The extensive parametric study shows that the standard deviation of C_L is 0.25. The results support to design active load control systems by highlighting the magnitude of C_L and α variations experienced by HAWTs, and thus the dC_L that needs to be delivered by an AFC system.