Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published September 2022 | public
Journal Article

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


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.

Additional Information

We would like to thank the field site team from ReNew Power who assisted with the experiment. M.F.H. acknowledges partial support from the MIT Energy Initiative and Siemens Gamesa Renewable Energy. J.O.D. acknowledges partial support from the California Institute of Technology. The authors would like to thank the reviewers for their thoughtful comments and contribution to this work. We would also like to thank G. Tregnago for thoughtful comments and contribution to this work.

Additional details

August 22, 2023
October 24, 2023