2026-04-17T18:09:48Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/74212024-05-14T11:44:00Zcom_10259_6155com_10259_4266com_10259.4_106com_10259_2604col_10259_6156
Repositorio Institucional de la Universidad de Burgos
author
Sierra Garcia, Jesús Enrique
author
Santos, Matilde
author
Pandit, Ravi
2023-02-08T09:23:28Z
2023-02-08T09:23:28Z
2022-05
0952-1976
http://hdl.handle.net/10259/7421
10.1016/j.engappai.2022.104769
Wind turbine (WT) pitch control is a challenging issue due to the non-linearities of the wind device and its
complex dynamics, the coupling of the variables and the uncertainty of the environment. Reinforcement learn-
ing (RL) based control arises as a promising technique to address these problems. However, its applicability
is still limited due to the slowness of the learning process. To help alleviate this drawback, in this work we
present a hybrid RL-based control that combines a RL-based controller with a proportional–integral–derivative
(PID) regulator, and a learning observer. The PID is beneficial during the first training episodes as the RL based
control does not have any experience to learn from. The learning observer oversees the learning process by
adjusting the exploration rate and the exploration window in order to reduce the oscillations during the training
and improve convergence. Simulation experiments on a small real WT show how the learning significantly
improves with this control architecture, speeding up the learning convergence up to 37%, and increasing the
efficiency of the intelligent control strategy. The best hybrid controller reduces the error of the output power
by around 41% regarding a PID regulator. Moreover, the proposed intelligent hybrid control configuration has
proved more efficient than a fuzzy controller and a neuro-control strategy.
eng
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Intelligent control
Reinforcement learning
Learning observer
Pitch control
Wind turbines
Wind turbine pitch reinforcement learning control improved by PID regulator and learning observer
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/7421/1/Sierra-eaai_2022.pdf
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3ffada7bd909ae0543611dbae0894760
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Sierra-eaai_2022.pdf