In wind farms, turbines are operated to maximize only their own power production. Individual operation results in wake losses that reduce farm energy. Here we operate a wind turbine array collectively to maximize array production through wake steering. We develop a physics-based, data-assisted flow control model to predict the power-maximizing control strategy. We first validate the model with a multi-month field experiment at a utility-scale wind farm. The model is able to predict the yaw-misalignment angles which maximize array power production within ± 5° for most wind directions (5–32% gains). Using the validated model, we design a control protocol which increases the energy production of the farm in a second multi-month experiment by 3.0% ± 0.7% and 1.2% ± 0.4% for wind speeds between 6 m s⁻¹ and 8 m s^−1  and all wind speeds, respectively. The predictive model can enable a wider adoption of collective wind farm operation.

在风电场中，涡轮机的运行仅是为了最大限度地提高自身的发电量。单独操作会导致尾流损失，从而减少农场能源。在这里，我们共同操作一个风力涡轮机阵列，以通过尾流转向最大限度地提高阵列产量。我们开发了一个基于物理的、数据辅助的流量控制模型来预测功率最大化控制策略。我们首先在公用事业规模的风电场进行了数月的现场实验来验证该模型。该模型能够预测偏航错位角，从而在大多数风向（5-32% 增益）下将阵列功率产生最大化在 ± 5° 内。^{使用经过验证的模型，我们设计了一个控制协议，在第二个多月实验中，对于 6 m s -1}和 8 m s之间的风速，该协议将农场的能源产量提高了 3.0% ± 0.7% 和 1.2% ± 0.4%^−1  和所有风速。预测模型可以更广泛地采用集体风电场运营。