## 1 Introduction  
Despite the booming market of the wind industry [1; 2; 3], the further exploitation of wind power is still challenged by many constraints, one of which is the high levelized cost. Since reducing the vortices in the wake area of a wind turbine can improve not only the power generation efficiency but also the reliability of the turbines in the wake area, active wake management (AWM) technology has been identified as a promising way to increase the power production, reduce operation and maintenance cost, and therefore cut down the levelized cost of a wind project [4]. The core thought of the AWM technology is to reduce the wake losses of the downstream wind turbines by performing the yaw operation of upstream turbines [5]. Such technology increases the added value of yaw control, which was originally designed to adapt the turbine to the changes in wind speed and wind direction [6]. In the implementation of the AWM technology, the wind turbines are required to work frequently at misaligned yaw angles in order to reduce the vortices in the wake area of the turbines. This may cause many uncertainties, not only to blade dynamics but also to the power generation performance of the turbines. To understand these uncertainties and the positive contribution of the AWM technology to power production, much effort has been made previously. For example, the aerodynamic characteristics and wake effects of wind turbines during yaw operation were investigated in [7; 8] using near-wake path reconstruction from wake tracking and also wind tunnel tests. Through conducting the measurement of a megawatt wind turbine, it was found that under neutral atmospheric conditions, the turbine misalignment would cause lateral deflections of its wake; moreover, the larger the yaw misalignment, the more the wake deflection will be [9]. In addition, the yaw operation also has a significant influence on both the power and thrust coefficients of the turbine, i.e., the yaw operation not only has the potential to improve the power generation of the wind farm but can also help to reduce the space of a wind farm [10]. Moreover, computational fluid dynamics (CFD) simulations have shown that the yaw operation can affect the comprehensive stress performance of the wind turbines [11]. When the rotor is yawed opposite to the shear layer of the incoming wake flow, the power output from the turbine will increase, while the yaw moments will decrease [12]. Furthermore, yaw control was also tried in order to change the directions and velocities of the wind in the wake area [13; 14]. Moreover, the yaw angles of the turbines were optimized in [15] for maximizing wind farm power production by using system engineering approaches. Simulation has revealed that power production can be improved by at least 5% through optimizing yaw offset [16]. However, the research in [17] has shown that the aeroelastic damping values of the blade could be reduced by 33% during yaw operation. Both fatigue and extreme loads have complex trends with yaw offsets [18]. For example, the turbine produces yaw moment during the revolution, and its maximum aerodynamic load appears at the upwind azimuth [19]; from the simulation results obtained based on an unstructured mesh flow solver, it was found that aerodynamic load of the blade can be significantly reduced, and blade load has periodic fluctuation during yaw operation [20]; the average power and thrust decrease with the increase of yaw angle, along with the increase of oscillation amplitude under large yaw angles [21; 22]; and so on. Considering that the load due to wind shear can be affected by yaw misalignment, the potential of alleviating blade load variations was also assessed. It was found that in deterministic inflow the blade load variations (steady-state) could be reduced by 70% [23]. In addition, it was found that the fluctuation of the root flapwise bending moment of rigid blades can be reduced by 84.5% through optimizing the yaw and pitch angles [24].