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