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A multi-agent reinforcement learning approach for wind farm frequency control
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Liang, Yanchang, Zhao, Xiaowei and Sun, Li (2023) A multi-agent reinforcement learning approach for wind farm frequency control. IEEE Transactions on Industrial Informatics, 19 (2). pp. 1725-1734. doi:10.1109/TII.2022.3182328 ISSN 1551-3203.
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WRAP-multi-agent-reinforcement-learning-approach-wind-farm-frequency-control-Liang-2022.pdf - Accepted Version - Requires a PDF viewer. Download (8Mb) | Preview |
Official URL: http://doi.org/10.1109/TII.2022.3182328
Abstract
As wind turbines (WTs) become more prevalent, there is an increasing interest in actively controlling their power output to participate in the frequency regulation for the power grid. Conventional frequency regulation controllers use fixed gains, making it difficult for the WT to adjust its kinetic energy uptake to its operating conditions and to collaborate effectively with other WTs in the wind farm. In addition, the design of conventional frequency controllers does not consider their impacts on mechanical structure. To address these issues, we model the cooperative frequency control problem for all WTs in a wind farm as a decentralised partially observable Markov decision process (Dec-POMDP) and use a multi-agent deep reinforcement learning (MADRL) algorithm to solve it. We also develop a grid-connected wind farm simulation model based on MATLAB/Simulink and OpenFAST, which can reflect the detailed interactions between the electrical and mechanical components of WTs. Simulation results show that the proposed strategy is effective in reducing frequency drops and has less impact on mechanical structure deflections compared with traditional methods.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > Q Science (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 plants , Electric power distribution -- Effect of wind power plants on, Wind power plants -- Design and construction, Wind turbines , Multiagent systems, Reinforcement learning | ||||||||
Journal or Publication Title: | IEEE Transactions on Industrial Informatics | ||||||||
Publisher: | IEEE | ||||||||
ISSN: | 1551-3203 | ||||||||
Official Date: | February 2023 | ||||||||
Dates: |
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Volume: | 19 | ||||||||
Number: | 2 | ||||||||
Page Range: | pp. 1725-1734 | ||||||||
DOI: | 10.1109/TII.2022.3182328 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | © 2022 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 | ||||||||
Copyright Holders: | IEEE | ||||||||
Date of first compliant deposit: | 16 June 2022 | ||||||||
Date of first compliant Open Access: | 16 June 2022 | ||||||||
RIOXX Funder/Project Grant: |
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