Control of a Wind-Turbine via Machine Learning techniques

L. Schena; E. Gillyns; W. Munters; S. Buckingham; M. A. Mendez

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arXiv · 2207.06206

Control of a Wind-Turbine via Machine Learning techniques

L. Schena·E. Gillyns·W. Munters·S. Buckingham·M. A. Mendez

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Abstract

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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L. Schena, E. Gillyns, W. Munters, S. Buckingham, M. A. Mendez. 2022-07-13. Control of a Wind-Turbine via Machine Learning techniques. https://arxiv.org/abs/2207.06206

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