| Author(s) | Howl, Michael F , Jesús Bas Quesada, Juan José Pena Martı́nez, Felipe Palou Larrañaga, Neeraj Yadav, Jasvipul S Chawla, Varun Sivaram, John O Dabiri |
| Journal | Nature Energy |
| Year | 2022 |
| DOI / Link | |
| Keywords |
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.