一种数据驱动的桨距角与转矩控制方法以提升风电场运行性能与效率

This paper proposes a Reversible Depth-wise Gated Network (RDG-Net) that controls pitch angle ( ) and generator torque ( {T_g} ) for wind farm (WF). The proposed RDG-Net can accurately forecast the and {T_g} outputs of wind turbines (WTs) for fatigue load suppression, power extraction and calculation efficiency improvement. The RDG-Net eliminates errors from linearization approximations in WT model, thus enhancing control performance. The method employs a reversible instance and depth-wise separable convolution (Revin-DSCNN) model for WTs. Then, a framework based on multi-head attention and gated graph recurrent neural networks (multi-GGRNNs) is adopted to model coupling relationships among WTs, such as the wake effect that significantly impacts performance. The proposed method replaces high-dimensional mathematical models and avoids computational complexity for joint modeling all WTs. Moreover, the RDG-Net models are implemented on distributed servers to gather real-time WF data for online training, thus enhancing adaptability and generalization ability. The effectiveness of the proposed RDG-Net is validated through simulations in MATLAB.