In response to the continuously increasing wind power penetration and growing individual unit capacity, this paper adopts a wind turbine (WT) classification-based active power allocation algorithm to reasonably distribute the wind farm (WF) power in order to minimize the power tracking error and smooth the output power fluctuation. The fuzzy c-means clustering algorithm (FCMA) is used to categorize the WTs and the regulation priority is determined based on the classification results. Moreover, for the purpose of enhancing the WF power control precision and grid dispatch response rapidity, model predictive control (MPC) is used in this article to perform coordinated control of WTs. The proposed control strategy is simulated on a WF with 10 wind turbines using MATLAB/Simulink and its practicability is validated.

wind farm; active power control; model predictive control; wind turbine classification; fuzzy c-means clustering algorithm