This article presents two model-free controllers for wind-turbine torque and pitch control. These controllers are based on reinforcement learning (RL) and Bayesian optimization (BO) and do not rely on any mathematical model of the wind-turbine dynamics, in contrast to classical approaches designed on linearized models. The model-free controllers were benchmarked against a proportional-integral-derivative (PID) regulator in a numerical environment using Blade Element Momentum theory for computing the aerodynamic torque and the blade loads. The results showed that the model-free approaches could increase power harvesting while reducing wind turbine loads.
@article{arxiv.2207.06206,
title = {Control of a Wind-Turbine via Machine Learning techniques},
author = {L. Schena and E. Gillyns and W. Munters and S. Buckingham and M. A. Mendez},
journal= {arXiv preprint arXiv:2207.06206},
year = {2022}
}