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Reinforcement learning-based wind farm control : towards large farm applications via automatic grouping and transfer learning
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Dong, Hongyang and Zhao, Xiaowei (2023) Reinforcement learning-based wind farm control : towards large farm applications via automatic grouping and transfer learning. IEEE Transactions on Industrial Informatics, 19 (12). 11833 -11845. doi:10.1109/TII.2023.3252540 ISSN 1551-3203.
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WRAP-reinforcement-learning-based-wind-farm-control-towards-large-farm-applications-via-automatic-grouping-transfer-learning-Dong-2023.pdf - Accepted Version - Requires a PDF viewer. Download (9Mb) | Preview |
Official URL: https://doi.org/10.1109/TII.2023.3252540
Abstract
The high system complexity and strong wake effects bring significant challenges to wind farm operations. Conventional wind farm control methods may lead to degraded power generation efficiency. A reinforcement learning (RL)-based approach is proposed in this paper to handle these issues, which can increase the long-term farm-level power generation subject to strong wake effects while without requiring analytical wind farm models. The proposed method is significantly distinct from existing RL-based wind farm control approaches, whose computational complexities usually increase heavily with the increase of total turbine numbers. In contrast, our method can greatly reduce training loads and enhance learning efficiency via two novel designs: (1) automatic grouping and (2) multi-agent-based transfer learning (MATL). Automatic Grouping can divide a large wind farm into small turbine groups by analyzing the aerodynamic interactions between turbines and utilizing some key principles from the graph theory. It enables the separated conduction of RL algorithms on small turbine groups, avoiding the complex training process and high computational costs of applying RL on the entire farm. Based on Automatic Grouping, MATL can further reduce the computational complexity by allowing agents (i.e. wind turbines) to inherit control policies under potential group changes. Case studies with a dynamical simulator show that the proposed method achieves clear power generation increases than the benchmark. It also dramatically reduces computational costs compared with typical RL-based wind farm control methods, paving the way for the application of RL in general wind farms.
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, Wind power plants, Machine learning, Wind turbines -- Aerodynamics, Electric power production, Reinforcement learning -- Computer simulation | ||||||||
Journal or Publication Title: | IEEE Transactions on Industrial Informatics | ||||||||
Publisher: | IEEE | ||||||||
ISSN: | 1551-3203 | ||||||||
Official Date: | December 2023 | ||||||||
Dates: |
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Volume: | 19 | ||||||||
Number: | 12 | ||||||||
Page Range: | 11833 -11845 | ||||||||
DOI: | 10.1109/TII.2023.3252540 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Re-use Statement: | © 2023 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 April 2023 | ||||||||
Date of first compliant Open Access: | 5 April 2023 | ||||||||
Funder: | Engineering and Physical Sciences Research Council (EPSRC) | ||||||||
Grant number: | EP/S000747/1 | ||||||||
RIOXX Funder/Project Grant: |
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