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Wind farm power generation control via double-network-based deep reinforcement learning
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Xie, Jingjie, Dong, Hongyang, Zhao, Xiaowei and Karcanias, Aris (2022) Wind farm power generation control via double-network-based deep reinforcement learning. IEEE Transactions on Industrial Informatics, 18 (4). pp. 2321-2330. doi:10.1109/TII.2021.3095563 ISSN 1551-3203.
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WRAP-wind-farm-power-generation-control-via-double-network-based-deep-reinforcement-learning-Dong-2021.pdf - Accepted Version - Requires a PDF viewer. Download (6Mb) | Preview |
Official URL: http://dx.doi.org/10.1109/TII.2021.3095563
Abstract
A model-free deep reinforcement learning (DRL) method is proposed in this paper to maximize the total power generation of wind farms through the combination of induction control and yaw control. Specifically, a novel double-network-based DRL approach is designed to generate control policies for thrust coefficients and yaw angles simultaneously and separately. Two sets of critic-actor networks are constructed to this end. They are linked by a central power-related reward, providing a coordinated control structure while inheriting the critic-actor mechanism's advantages. Compared with conventional DRL methods, the proposed double-network-based DRL strategy can adapt to the distinctive and incompatible features of different control inputs, guaranteeing a reliable training process and ensuring superior performance. Also, the prioritized experience replay strategy is utilized to improve the training efficiency of deep neural networks. Simulation tests based on a dynamic wind farm simulator show that the proposed method can significantly increase the power generation for wind farms with different layouts.
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 |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Reinforcement learning , Machine learning, Wind power, Wind power -- Automatic control, Intelligent control systems | ||||||||
Journal or Publication Title: | IEEE Transactions on Industrial Informatics | ||||||||
Publisher: | IEEE | ||||||||
ISSN: | 1551-3203 | ||||||||
Official Date: | April 2022 | ||||||||
Dates: |
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Volume: | 18 | ||||||||
Number: | 4 | ||||||||
Page Range: | pp. 2321-2330 | ||||||||
DOI: | 10.1109/TII.2021.3095563 | ||||||||
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: | 13 July 2021 | ||||||||
Date of first compliant Open Access: | 14 July 2021 | ||||||||
RIOXX Funder/Project Grant: |
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