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

TL;DR

This paper presents a physics-based, data-assisted flow control model that, when validated through multi-month field experiments, enables collective wind farm operation to significantly increase total energy production by optimizing wake steering.

Contribution

It introduces a novel, validated predictive model for collective wind farm control that improves energy output compared to individual turbine operation.

Findings

Flow control model predicts optimal yaw angles within +/- 5 degrees.

Farm energy production increased by 2.7% and 1.0% in experiments.

Model enables more efficient, collective wind farm operation.

Abstract

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.