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Multi-agent reinforcement learning control of a hydrostatic wind turbine-based farm
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Huang, Yubo, Lin, Shuyue and Zhao, Xiaowei (2023) Multi-agent reinforcement learning control of a hydrostatic wind turbine-based farm. IEEE Transactions on Sustainable Energy, 14 (4). pp. 2406-2416. doi:10.1109/tste.2023.3270761 ISSN 1949-3037.
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Official URL: https://doi.org/10.1109/tste.2023.3270761
Abstract
This paper leverages multi-agent reinforcement learning (MARL) to develop an efficient control system for a wind farm comprising a new type of wind turbines with hydrostatic transmission. The primary motivation for hydrostatic wind turbines (HWT) is increased reliability, and reduced manufacturing, operating, and maintaining costs by removing troublesome components and reducing nacelle weight. Nevertheless, the high system complexity of HWT and the wake effect pose significant challenges for the control of HWT-based wind farms. We therefore propose a MARL algorithm named multi-agent policy optimization (MAPO), which allows agents (turbines) to gradually improve their control policies by repeatedly interacting with the environment to learn an optimal operation curve for wind farms. Simulation results based on a wind farm simulator, FAST.Farm, show that MAPO outperforms the greedy policy and a popular learning-based method, multi-agent deep deterministic policy gradient (MADDPG), in terms of power generation.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
SWORD Depositor: | Library Publications Router | ||||||||
Journal or Publication Title: | IEEE Transactions on Sustainable Energy | ||||||||
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | ||||||||
ISSN: | 1949-3037 | ||||||||
Official Date: | October 2023 | ||||||||
Dates: |
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Volume: | 14 | ||||||||
Number: | 4 | ||||||||
Page Range: | pp. 2406-2416 | ||||||||
DOI: | 10.1109/tste.2023.3270761 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Re-use Statement: | © 2023 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: | 19 May 2023 | ||||||||
Date of first compliant Open Access: | 19 May 2023 | ||||||||
RIOXX Funder/Project Grant: |
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