The emergence of flexible AC transmission technology provides a new technical means for ensuring the reliable grid connection and stable operation of wind farms. Among them, the static reactive power generator has a fast response speed, which can accurately compensate for the reactive power of the wind farm and improve the power factor; this is widely used in wind farms. To obtain accurate static var generator (SVG) parameters to meet the reliability requirements of a power system, we propose an adaptive estimation method that considers the wind speed fluctuation of wind farms. First, analyzing the dynamic SVG characteristics allowed us to establish a mathematical model. Then, the corresponding relationship between the sensitivity values of the parameters to be identified and the fluctuating wind speed was established, and low and high wind speed models were constructed. Finally, for accurate estimation considering wind speed fluctuation, the parameter initial values are obtained by combining the low wind speed and high wind speed model identification parameters, and we introduce the multimode hybrid estimation of the SVG parameters, providing a new method for accurately identifying the SVG model parameters. The simulation results of the parameter estimation demonstrate the accuracy and stability of the proposed method.

A severe voltage sag on the wind‐farm grid side with doubly fed induction generators (DFIGs) can induce a peak inrush current in the rotors and damage converters, resulting in wind‐turbine disconnection from the grid. To prevent this from happening, a variable‐step model predictive control (VS‐MPC) strategy is proposed for improving the wind turbines’ ability to operate without disconnecting themselves from the grid when a fault occurs. First, the predictive‐control state‐space model of a doubly fed wind farm is established according to its working principle. Second, model predictive control (MPC) is applied on the rotor side of the DFIG to realize the rapid tracking of the rotor current to the reference value during low‐voltage ride‐through (LVRT) of the DFIG. Finally, a variable‐step size algorithm is introduced into the MPC controller to change the step size continuously during the LVRT period. This increases the control accuracy, realizing rapid attenuation of each transient component, whereby the wind‐farm LVRT capability is enhanced. The proposed control strategy was simulated and verified using MATLAB/Simulink. The simulation results indicated that VS‐MPC can effectively handle LVRT, allowing the recovery of a wind farm that uses DFIGs and improving the performance of wind‐farm.

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