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Wind-farm power tracking via preview-based robust reinforcement learning
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Dong, Hongyang and Zhao, Xiaowei (2022) Wind-farm power tracking via preview-based robust reinforcement learning. IEEE Transactions on Industrial Informatics, 18 (3). pp. 1706-1715. doi:10.1109/TII.2021.3093300 ISSN 1551-3203.
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WRAP-Wind-farm-power-tracking-via-preview-based-robust-reinforcement-learning-2021.pdf - Accepted Version - Requires a PDF viewer. Download (4Mb) | Preview |
Official URL: https://doi.org/10.1109/TII.2021.3093300
Abstract
This paper aims to address the wind-farm power tracking problem, which requires the farm's total power generation to track time-varying power references and therefore allows the wind farm to participate in ancillary services such as frequency regulation. A novel preview-based robust deep reinforcement learning (PR-DRL) method is proposed to handle such tasks which are subject to uncertain environmental conditions and strong aerodynamic interactions among wind turbines. To our knowledge, this is for the first time that a data-driven model-free solution is developed for wind-farm power tracking. Particularly, reference signals are treated as preview information and embedded in the system as specially designed augmented states. The control problem is then transformed into a zero-sum game to quantify the influence of unknown wind conditions and future reference signals. Built upon the $H_\infty$ control theory, the proposed PR-DRL method can successfully approximate the resulting zero-sum game's solution and achieve wind-farm power tracking. Time-series measurements and long short-term memory (LSTM) networks are employed in our DRL structure to handle the non-Markovian property induced by the time-delayed feature of aerodynamic interactions. Tests based on a dynamic wind farm simulator demonstrate the effectiveness of the proposed PR-DRL wind farm control strategy.
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 T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Wind power , Reinforcement learning, Intelligent control systems , Wind power plants -- Automatic control | ||||||||
Journal or Publication Title: | IEEE Transactions on Industrial Informatics | ||||||||
Publisher: | IEEE | ||||||||
ISSN: | 1551-3203 | ||||||||
Official Date: | March 2022 | ||||||||
Dates: |
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Volume: | 18 | ||||||||
Number: | 3 | ||||||||
Page Range: | pp. 1706-1715 | ||||||||
DOI: | 10.1109/TII.2021.3093300 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | © 2021 IEEE. 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: | 3 July 2021 | ||||||||
Date of first compliant Open Access: | 7 July 2021 | ||||||||
RIOXX Funder/Project Grant: |
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