2026-06-27T22:45:12Zhttps://docta.ucm.es/rest/oai/request
oai:docta.ucm.es:20.500.14352/1122472025-08-27T13:22:37Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense author Sierra-García, Jesús Enrique author Santos Peñas, Matilde 2024-12-09T14:54:10Z 2024-12-09T14:54:10Z 2020 Sierra-García, J. E., & Santos, M. (2020). Exploring reward strategies for wind turbine pitch control by reinforcement learning. Applied Sciences, 10(21), 7462. 10.3390/app10217462 https://hdl.handle.net/20.500.14352/112247 https://doi.org/10.3390/app10217462 In this work, a pitch controller of a wind turbine (WT) inspired by reinforcement learning (RL) is designed and implemented. The control system consists of a state estimator, a reward strategy, a policy table, and a policy update algorithm. Novel reward strategies related to the energy deviation from the rated power are defined. They are designed to improve the efficiency of the WT. Two new categories of reward strategies are proposed: “only positive” (O-P) and “positive-negative” (P-N) rewards. The relationship of these categories with the exploration-exploitation dilemma, the use of ϵ-greedy methods and the learning convergence are also introduced and linked to the WT control problem. In addition, an extensive analysis of the influence of the different rewards in the controller performance and in the learning speed is carried out. The controller is compared with a proportional-integral-derivative (PID) regulator for the same small wind turbine, obtaining better results. The simulations show how the P-N rewards improve the performance of the controller, stabilize the output power around the rated power, and reduce the error over time. eng Attribution-NonCommercial-NoDerivatives 4.0 International Exploring reward strategies for wind turbine pitch control by reinforcement learning journal article 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URL https://docta.ucm.es/bitstreams/34973ecb-1432-46f2-bfeb-ff051c31f5ec/download File MD5 90d3e26a0857e06ea21b5f968ce15b7c 4341459 application/pdf applsci-10-07462-v3.pdf