The atmospheric stability and ground topography play an important role in shaping wind-speed profiles. However, the commonly used power-law wind-speed extrapolation method is usually applied, ignoring atmospheric stability effects. In the present work, a new power-law wind-speed extrapolation method based on atmospheric stability classification is proposed and evaluated for flows over different types of terrain. The method uses the wind shear exponent estimated in different stability conditions rather than its average value in all stability conditions. Four stability classification methods, namely the Richardson Gradient (RG) method, the Wind Direction Standard Deviation (WDSD) method, the Wind Speed Ratio (WSR) method and the Monin–Obukhov (MO) method are applied in the wind speed extrapolation method for three different types of terrain. Tapplicability is analyzed by comparing the errors between the measured data and the calculated results at the hub height. It is indicated that the WSR classification method is effective for all the terrains investigated while the WDSD method is more suitable in plain areas. Moreover, the RG and MO methods perform better in complex terrains than the other methods, if two-level temperature data are available.

On a wind farm, the wake has an important impact on the performance of the wind turbines. For example, the wake of an upstream wind turbine affects the blade load and output power of the downstream wind turbine. In this paper, a modified actuator line model with blade tips, root loss, and an airfoil three-dimensional delayed stall was revised. This full-scale modified actuator line model with blades, nacelles, and towers, was combined with a Large Eddy Simulation, and then applied and validated based on an analysis of wind turbine wakes in wind farms. The modified actuator line model was verified using an experimental wind turbine. Subsequently, numerical simulations were conducted on two NREL 5 MW wind turbines with different staggered spacing to study the effect of the staggered spacing on the characteristics of wind turbines. The results show that the output power of the upstream turbine stabilized at 5.9 MW, and the output power of the downstream turbine increased. When the staggered spacing is R and 1.5R, both the power and thrust of the downstream turbine are severely reduced. However, the length of the peaks was significantly longer, which resulted in a long-term unstable power output. As the staggered spacing increased, the velocity in the central near wake of the downstream turbine also increased, and the recovery speed at the threshold of the wake slowed down. The modified actuator line model described herein can be used for the numerical simulation of wakes in wind farms.

In recent years, a major focus on wind farm wake control is to maximise the production of wind farms. To improve the power generation efficiency of wind farms through wake regulation, this study investigates yaw optimisation for wind farm production maximisation from the perspective of time-varying wakes. To this end, we first deduce a simplified dynamic wake model according to the momentum conservation theory and backward difference method. The accuracy of the proposed model is verified by simulation comparisons. Then, the time lag of wake propagation and its impact on wind farm production maximisation through wake meandering is analysed. On this basis, a yaw optimisation method for increasing wind farm energy capture is presented. This optimisation method uses the proposed dynamic wake model for wind farm prediction. The results indicate that the optimisation period is critical to the effect of the optimisation method on wind farm energy capture.

This study aims to maintain the frequency stability of the power system penetrated by wind power. Hence, a battery energy storage system (BESS) is applied to smooth the wind power output in power systems and to enhance their load frequency control (LFC) capacity. A novel comprehensive control framework is proposed for power systems integrated with wind farms and BESS based on an adaptive fuzzy super-twisting sliding mode control (AF-SSMC) method. Firstly, the sliding functions and control laws of subsystems are designed according to different relative degrees. Then, the super-twisting algorithm is applied to suppress the chattering of the sliding mode control law. Furthermore, an adaptive fuzzy control method is used to adjust the control gains online for better control performance of the controllers. The Lyapunov stability theory is employed to prove the asymptotic stability of the subsystems. The model of an interconnected thermal power system with wind and BESS penetration is also constructed for simulation analyses. The results indicate that the AF-SSMC method effectively reduces the chattering, and the proposed framework stabilizes the frequency of the power system under system uncertainties and external disturbances. Moreover, the wind farm and BESS combined system accurately tracks a reference power to reduce wind power fluctuations.

The yaw of a large-scale wind turbine will change the aerodynamic performance of its substructures. In view of this, this article applied a LBM-LES method to numerically simulate the yawed MEXICO wind turbine and compared the numerical results with the experimental data collected in the New MEXICO experiment to verify the reliability of the method. A rapid dynamic yaw is defined with a yaw speed of 5°/s in yaw control, while a slow dynamic yaw is with a yaw speed of 0.3°/s. The NREL 5 MW offshore wind turbine was used to explore the constant wake characteristics under the conditions of a rapid dynamic yaw and of a slow dynamic yaw. It can be seen from the results that the LBM-LES method captures the detailed characteristics of the complex unsteady flow field of the wind turbine. In the rapid dynamic yaw process, the transition area between the far wake and the near wake shows a large deflection. However, in the process of the slow dynamic yaw, the far wake deflects forward relatively to the yaw. The dynamic yaw wake forms a kidney-shaped cross-section similar to the one in a steady yaw. The yaw wake recovers faster than the wake without yaw, and the rapid dynamic yaw wake has an asymmetric structure. The thrust of the wind turbine drops rapidly during the rapid dynamic yaw, while the thrust of the slow dynamic yaw decreases gradually. The thrust drops three times per rotor revolution, and the time of thrust decrease is delayed as the yaw angle increases.

Monin–Obukhov similarity theory (MOST) overestimates the mean vertical velocity gradient in some atmospheric stable conditions, i.e., Richardson number $R_f<0.25$ . To obtain a given hub-height inflow velocity for a certain roughness length, this overestimated velocity gradient underpredicts the friction wind speed and the turbulence intensity, potentially influencing wake modeling of a wind turbine. This work investigates the side effects of the breakdown of MOST on wake modeling under stable conditions and makes some modifications to the flow similarity functions to eliminate these side effects. Based on a field measurement in a wind farm, we first show that MOST predicts a larger velocity gradient for the atmospheric stability parameter $\zeta >0.1$ and proposes new flow similarity functions without constraining $R_f$ to limit the overestimated velocity gradient. Next, different turbulence models based on MOST and a modified one based on the new similarity functions are investigated through numerical simulations. These turbulence models are combined with the actuator disk model (AD) and Reynolds-averaged Navier–Stokes equations (RANS) to model wind turbine wakes under stable conditions. As compared to measurements, numerical results show that turbulence models based on MOST result in a larger wake deficit and a slower wake recovery rate with a root-mean-squared error (RSME) of wake deficit in the range of 0.07 to 0.20. This overestimated wake effect is improved by applying the new similarity functions, and the RSME of wake deficit is reduced by 0.05 on average.