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Data-driven wind farm control via multi-player deep reinforcement learning
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Dong, Hongyang and Zhao, Xiaowei (2023) Data-driven wind farm control via multi-player deep reinforcement learning. IEEE Transactions on Control Systems Technology, 31 (3). pp. 1468-1475. doi:10.1109/TCST.2022.3223185 ISSN 1063-6536.
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WRAP-Data-driven-wind-farm-control-via-multi-player-deep-reinforcement-learning-Dong-2022.pdf - Accepted Version - Requires a PDF viewer. Download (2121Kb) | Preview |
Official URL: https://doi.org/10.1109/TCST.2022.3223185
Abstract
This brief paper proposes a novel data-driven control scheme to maximize the total power output of wind farms subject to strong aerodynamic interactions among wind turbines. The proposed method is model-free and has strong robustness, adaptability and applicability. Particularly, distinct from state-of-the-art data-driven wind farm control methods that commonly employ the steady-state or time-averaged data (such as turbines' power outputs under steady wind conditions or from steady-state models) to carry out learning, the proposed method directly mines in-depth the time-series data measured at turbine rotors under time-varying wind conditions to achieve farm-level power maximization. The control scheme is built on a novel multi-player deep reinforcement learning method (MPDRL), in which a special critic-actor-distractor structure, along with deep neural networks (DNNs), is designed to handle the stochastic feature of wind speeds and learn optimal control policies subject to a user-defined performance metric.
The effectiveness, robustness and scalability of the proposed MPDRL-based wind farm control method are tested by prototypical case studies with a dynamic wind farm simulator. Compared with the commonly employed greedy strategy, the proposed method leads to clear increases in farm-level power generation in case studies.
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): | Wind power, Wind power plants, Wind turbines, Machine learning, Reinforcement learning | |||||||||
Journal or Publication Title: | IEEE Transactions on Control Systems Technology | |||||||||
Publisher: | IEEE | |||||||||
ISSN: | 1063-6536 | |||||||||
Official Date: | May 2023 | |||||||||
Dates: |
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Volume: | 31 | |||||||||
Number: | 3 | |||||||||
Page Range: | pp. 1468-1475 | |||||||||
DOI: | 10.1109/TCST.2022.3223185 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
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 | |||||||||
Copyright Holders: | IEEE | |||||||||
Date of first compliant deposit: | 13 February 2023 | |||||||||
Date of first compliant Open Access: | 13 February 2023 | |||||||||
RIOXX Funder/Project Grant: |
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