In a wind farm, the wake interactions between turbines can significantly reduce the power output of the wind farm. Cooperative wind farm control considering the interactions can improve the power output of the overall wind farm. However, in general, only a simplified wind farm power generation model can be available due to the highly complex nature of the interactions. This leads to model mismatch and limits the performance of model-based methods. This paper proposes an adaptive calibration model based power optimization method for cooperative wind farm control to handle the model mismatch. The proposed method mainly consists of two iterative phases, i.e., adaptation and optimization. In the adaptation phase, the model mismatch is approximated using neural network with great nonlinear fitting capability, and a calibrated model is then obtained. This phase fully exploits simplified yet computationally tractable power generation model and readily available power generation data from the wind farm. To improve the efficiency of the adaptation phase, a sample data utilization method is developed via mining knowledge from the historical measurements of real wind farm. In the optimization phase, the control actions of all wind turbines are determined using calibrated model, achieving robust power optimization for wind farm. The simulation results demonstrate the effectiveness of the proposed method in compensating for the effect of model mismatch on the wind farm power generation performance.

Generally, voltage support at the point of common coupling (PCC) of a wind farm is achieved through centralized static var generators (SVGs). Since the reactive power requirements occupy their capacity in a steady state, the reactive power support capacity of the SVG is limited during high voltage ride through (HVRT) or low voltage ride through (LVRT). While wind turbines can provide voltage support in accordance with the grid code, their responses are usually delayed due to communication and transmission lags. To enhance the dynamic performance of wind farms during fault ride-through, a reactive power substitution (RPS) control strategy is proposed in this paper. In a steady state, this RPS control method preferentially utilizes the remaining capacity of wind turbines to substitute for the output of the SVG. Considering differences in terminal voltage characteristics and operating conditions, this RPS control method employs a particle swarm optimization (PSO) algorithm to ensure that wind turbines can provide their optimal reactive power support capacity. When the grid voltage swells or drops, the SVG has a sufficient reactive power reserve to support the grid quickly. This paper utilizes a regional power grid incorporating two wind farms connected to different buses as a case study to validate this RPS control strategy.