2026-04-17T16:41:07Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/74212024-05-14T11:44:00Zcom_10259_6155com_10259_4266com_10259.4_106com_10259_2604col_10259_6156

Sierra Garcia, Jesús Enrique Santos, Matilde 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 http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess 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