基于尾流模型动态校准的虚实结合风电场最大功率发电控制

Due to the time delay characteristic of wake effect, the future state information of downstream wind turbines (WTs) is required for wind farm (WF) dynamic optimization but cannot be directly measured. To address the issue, this study proposes a novel virtuality-reality combination control scheme for WF dynamic maximum power generation control (DMPGC). The wind speed in VWF is calculated by wake model without time delay, thus the future state information corresponding to the current freestream wind speed of RWF can be obtained in advance. To ensure consistency of state information between RWF and VWF, meanwhile to enhance the precision of WF optimization model, a wake model dynamic calibration method is proposed to improve the prediction accuracy of wake wind speed. Thereafter, an active wake control strategy based on calibrated wake model is implemented to maximize the total power generation of VWF, and the optimal control commands are delay dispatched to RWF according to the wake delay time. Simulation results show that the proposed scheme improves calculation accuracy of wake model, increases total power generation and owns better fatigue load distribution of WF under different wind conditions.