Summary

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.

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Published on 24/11/22
Accepted on 24/11/22
Submitted on 24/11/22

Volume Industrial Applications, 2022
DOI: 10.23967/eccomas.2022.297
Licence: CC BY-NC-SA license

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