2022/01/26 by Michael F. Howland, Jesús Bas Quesada, Howland, Michael F. +13 · 1 citation
Engineering · #Energy Load and Power Forecasting #FOS: Mathematics #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Optimization and Control (math.OC) #Wind Energy Research and Development #Wind Turbine Control Systems
paper · pdf · doi:10.48550/arxiv.2202.06683
openalex publication_date 2022/01/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.