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Wind turbines located in wind farms are operated to maximize only their ownpower production. Individual operation results in wake losses that reduce farmenergy. In this study, we operate a wind turbine array collectively to maximizetotal array production through wake steering. The selection of the farm controlstrategy relies on the optimization of computationally efficient flow models.We develop a physics-based, data-assisted flow control model to predict theoptimal control strategy. In contrast to previous studies, we first design andimplement a multi-month field experiment at a utility-scale wind farm tovalidate the model over a range of control strategies, most of which aresuboptimal. The flow control model is able to predict the optimal yawmisalignment angles for the array within +/- 5 degrees for most wind directions(11-32% power gains). Using the validated model, we design a control protocolwhich increases the energy production of the farm in a second multi-monthexperiment by 2.7% and 1.0%, for the wind directions of interest and for windspeeds between 6 and 8 m/s and all wind speeds, respectively. The developed andvalidated predictive model can enable a wider adoption of collective wind farmoperation.

Collective wind farm operation based on a predictive model increases utility-scale energy production