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Power regulation and load mitigation of floating wind turbines via reinforcement learning
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Xie, Jingjie, Dong, Hongyang and Zhao, Xiaowei (2023) Power regulation and load mitigation of floating wind turbines via reinforcement learning. IEEE Transactions on Automation Science and Engineering . doi:10.1109/TASE.2023.3295576 ISSN 1545-5955. (In Press)
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WRAP-Power-regulation-load-mitigation-floating-wind-turbines-reinforcement-23.pdf - Accepted Version - Requires a PDF viewer. Download (3549Kb) | Preview |
Official URL: https://doi.org/10.1109/TASE.2023.3295576
Abstract
Floating offshore wind turbines (FOWTs) are often subjected to heavy structural loads due to challenging operating conditions, which can negatively impact power generation and lead to structural fatigue. This paper proposes a novel reinforcement learning (RL)-based control scheme to address this issue. It combines individual pitch control (IPC) and collective pitch control (CPC) to balance two key objectives: load reduction and power regulation. Specifically, a novel incremental model-based dual heuristic programming (IDHP) strategy is developed as the IPC solution to reduce structural loads. It integrates the online-learned FOWT dynamics into the dual heuristic programming process, making the entire control scheme data-driven and free from dependence on analytical models. Furthermore, the proposed method differs from existing IDHP methods in that only partial system dynamics need to be learned, resulting in a simplified design structure and improved training efficiency. Tests using a high-fidelity FOWT simulator demonstrate the effectiveness of the proposed method.
Item Type: | Journal Article | ||||||
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Subjects: | T Technology > T Technology (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): | Intelligent control systems, Wind turbines, Reinforcement learning, Wind power | ||||||
Journal or Publication Title: | IEEE Transactions on Automation Science and Engineering | ||||||
Publisher: | IEEE | ||||||
ISSN: | 1545-5955 | ||||||
Official Date: | 2023 | ||||||
Dates: |
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DOI: | 10.1109/TASE.2023.3295576 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | In Press | ||||||
Reuse Statement (publisher, data, author rights): | © 2023 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 | ||||||
Date of first compliant deposit: | 21 July 2023 | ||||||
Date of first compliant Open Access: | 21 July 2023 | ||||||
RIOXX Funder/Project Grant: |
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