Flow control devices have been introduced in the wind energy sector to improve the aerodynamic behavior of the wind turbine blades (WTBs). Among these flow control devices, Gurney flaps (GFs) have been the focus of innovative research, due to their good characteristics which enhance the lift force that causes the rotation of the wind turbine rotor. The lift force increment introduced by GFs depends on the physical characteristics of the device and the angle of attack (AoA) of the incoming wind. Hence, despite a careful and detailed design, the real performance of the GFs is conditioned by an external factor, the wind. In this paper, an active operation of GFs is proposed in order to optimize their performance. The objective of the active Gurney flap (AGF) flow control technique is to enhance the aerodynamic adaption capability of the wind turbine and, thus, achieve an optimal operation in response to fast variations in the incoming wind. In order to facilitate the management of the information used by the AGF strategy, the aerodynamic data calculated by computational fluid dynamics (CFD) are stored in an artificial neural network (ANN). Blade element momentum (BEM) based calculations have been performed to analyze the aerodynamic behavior of the WTBs with the proposed AGF strategy and calculate the corresponding operation of the wind turbine. Real wind speed values from a meteorological station in Salt Lake City, Utah, USA, have been used for the steady BEM calculations. The obtained results show a considerable improvement in the performance of the wind turbine, in the form of an enhanced generated energy output value and a reduced bending moment at the root of the WTB.
Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control