Fuzzy-based collective pitch control for wind turbine via deep reinforcement learning.

Autor: Nabeel A; Electric Power Department - Faculty of Engineering - Cairo University, Giza 12613, Egypt. Electronic address: Abdelhamid.Nabeel@eng.cu.edu.eg., Lasheen A; Electric Power Department - Faculty of Engineering - Cairo University, Giza 12613, Egypt. Electronic address: Ahmed_lasheen59@cu.edu.eg., Elshafei AL; Electric Power Department - Faculty of Engineering - Cairo University, Giza 12613, Egypt. Electronic address: elshafei@eng.cu.edu.eg., Aboul Zahab E; Electric Power Department - Faculty of Engineering - Cairo University, Giza 12613, Egypt. Electronic address: zahab0@eng.cu.edu.eg.
Jazyk: angličtina
Zdroj: ISA transactions [ISA Trans] 2024 May; Vol. 148, pp. 307-325. Date of Electronic Publication: 2024 Mar 26.
DOI: 10.1016/j.isatra.2024.03.023
Abstrakt: Wind turbines (WTs) have highly nonlinear and uncertain dynamics due to aerodynamic complexity, mechanical factors, and fluctuations in wind conditions. Turbulence and wind shear add complexity to modelling, especially in constant power region (region 3). Thus, an effective control design demands a deep understanding of the nonlinearities and uncertainties. This paper suggests a novel model-free reinforcement learning (RL) collective pitch angle controller to operate efficiently in region 3. The proposed controller stabilizes generator speed, maximizes power output, and minimizes fluctuations while accommodating system uncertainties, nonlinearity, and pitch limits. The disparity between WT dynamics due to wind speed perturbations and uncertainties is measured using a gap-metric criterion. The controller design adopts a deep deterministic policy gradient (DDPG) algorithm to train six agents in a medium-fidelity WT environment at different mean wind speeds to ensure the controller's robustness. Initially, imitation learning is used for efficient sample collection to fasten training convergence. Afterwards, the agent learns by interacting with the environment. After the training, the pitch control outputs from multi-trained agents are processed by a fuzzy system to have smooth transitions under different operating conditions. The resulting fuzzy DDPG (F-DDPG) controller is deployed to obtain the optimal pitch control action. The performance of the proposed F-DDPG controller is compared to the gain-scheduled PI (GSPI), Linear-Quadratic-Regulator (LQR), and single-DDPG-agent controllers. The controllers are simulated in high-fidelity onshore and offshore 5-MW WT environments using the OpenFAST/MATLAB simulation tools. The results reveal the superiority of the proposed controller in generalizing its optimal performance in different operating conditions.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 ISA. Published by Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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