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Wind turbine fault-tolerant control via incremental model-based reinforcement learning
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Xie, Jingjie, Dong, Hongyang, Zhao, Xiaowei and Lin, Shuyue (2024) Wind turbine fault-tolerant control via incremental model-based reinforcement learning. IEEE Transactions on Automation Science and Engineering . doi:10.1109/TASE.2024.3372713 ISSN 1545-5955. (In Press)
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Official URL: https://doi.org/10.1109/TASE.2024.3372713
Abstract
A reinforcement learning (RL) based fault-tolerant control strategy is developed in this paper for wind turbine torque & pitch control under actuator & sensor faults subject to unknown system models. An incremental model-based heuristic dynamic programming (IHDP) approach, along with a critic-actor structure, is designed to enable fault-tolerance capability and achieve optimal control. Particularly, an incremental model is embedded in the critic-actor structure to quickly learn the potential system changes, such as faults, in real-time. Different from the current IHDP methods that need the intensive evaluation of the state and input matrices, only the input matrix of the incremental model is dynamically evaluated and updated by an online recursive least square estimation procedure in our proposed method. Such a design significantly enhances the online model evaluation efficiency and control performance, especially under faulty conditions. In addition, a value function and a target critic network are incorporated into the main critic-actor structure to improve our method’s learning effectiveness. Case studies for wind turbines under various working conditions are conducted based on the fatigue, aerodynamics, structures, and turbulence (FAST) simulator to demonstrate the proposed method’s solid fault-tolerance capability and adaptability. Note to Practitioners —This work achieves high-performance wind turbine control under unknown actuator & sensor faults. Such a task is still an open problem due to the complexity of turbine dynamics and potential uncertainties in practical situations. A novel data-driven and model-free control strategy based on reinforcement learning is proposed to handle these issues. The designed method can quickly capture the potential changes in the system and adjust its control policy in real-time, rendering strong adaptability and fault-tolerant abilities. It provides data-driven innovations for complex operational tasks of wind turbines and d...
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > Q Science (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Wind turbines -- Automatic control, Reinforcement learning, Heuristic programming, Intelligent control systems, Fault tolerance (Engineering) | ||||||||
Journal or Publication Title: | IEEE Transactions on Automation Science and Engineering | ||||||||
Publisher: | IEEE | ||||||||
ISSN: | 1545-5955 | ||||||||
Official Date: | 2024 | ||||||||
Dates: |
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DOI: | 10.1109/TASE.2024.3372713 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | In Press | ||||||||
Re-use Statement: | © 2024 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Copyright Holders: | IEEE | ||||||||
Date of first compliant deposit: | 25 March 2024 | ||||||||
Date of first compliant Open Access: | 25 March 2024 | ||||||||
RIOXX Funder/Project Grant: |
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