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Intelligent wind farm control via grouping-based reinforcement learning
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Dong, Hongyang and Zhao, Xiaowei (2022) Intelligent wind farm control via grouping-based reinforcement learning. In: 2022 European Control Conference (ECC), London, UK, 12-15 Jul 2022 pp. 993-998. ISBN 9783907144077. doi:10.23919/ECC55457.2022.9838151
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WRAP-intelligent-wind-farm-control-via-grouping-based-reinforcement-learning-Dong-2022.pdf - Accepted Version - Requires a PDF viewer. Download (1919Kb) | Preview |
Official URL: https://doi.org/10.23919/ECC55457.2022.9838151
Abstract
This paper aims to maximize the total power generation for wind farms subject to strong wake effects and stochastic inflow wind speeds. A data-driven control method that only requires the accessible measurements of every turbine in the farm is proposed via deep reinforcement learning (DRL). We employ a grouping strategy to mitigate the high computational complexity induced by DRL and enhance our method’s applicability to large-scale wind farms. Based on the levels of aerodynamic interactions among turbines, this grouping strategy divides the whole farm into small sub-groups. Therefore, one can execute DRL on these sub-groups instead of carrying on a complicated learning process for the entire farm. Simulations verify the advantages of the proposed DRL-based wind farm control method over the commonly employed greedy strategy. Results also show that the proposed method can significantly reduce the overall computing cost compared with the direct execution of DRL on the whole wind farm.
Item Type: | Conference Item (Paper) | |||||||||
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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 , Reinforcement learning , Wind power | |||||||||
Publisher: | IEEE | |||||||||
ISBN: | 9783907144077 | |||||||||
Official Date: | 5 August 2022 | |||||||||
Dates: |
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Page Range: | pp. 993-998 | |||||||||
DOI: | 10.23919/ECC55457.2022.9838151 | |||||||||
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 | |||||||||
Date of first compliant deposit: | 3 September 2022 | |||||||||
Date of first compliant Open Access: | 6 September 2022 | |||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | |||||||||
Title of Event: | 2022 European Control Conference (ECC) | |||||||||
Type of Event: | Conference | |||||||||
Location of Event: | London, UK | |||||||||
Date(s) of Event: | 12-15 Jul 2022 |
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