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Reinforcement learning-based inertia and droop control for wind farm frequency regulation
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Liang, Yanchang, Sun, Li and Zhao, Xiaowei (2022) Reinforcement learning-based inertia and droop control for wind farm frequency regulation. In: 2022 IEEE Power & Energy Society General Meeting (PESGM), Denver, CO, USA, 17-21 Jul 2022 ISBN 9781665408233. doi:10.1109/PESGM48719.2022.9917075 ISSN 1944-9933.
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WRAP-reinforcement-learning-based-inertia-droop-control-wind-farm-frequency-regulation-Zhao-2022.pdf - Accepted Version - Requires a PDF viewer. Download (3993Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/PESGM48719.2022.9917075
Abstract
As more and more wind turbines (WTs) are installed, there is an increasing interest in actively controlling their power output to meet power set-points and to participate in the frequency regulation for the utility grid. Conventional inertial and droop control loops use fixed gains, making it difficult to utilise the kinetic energy of WTs in a wind farm in a synergistic manner based on real-time information. In this paper, the fixed gains are modified to adaptive gains to improve frequency support performance and reduce the impact on mechanical structures. The cooperative frequency control problem for all WTs in a wind farm is modelled as a decentralised partially observable Markov decision process (Dec-POMDP) and solved using a multi-agent deep reinforcement learning (MADRL) algorithm. MATLAB/Simulink and FAST are run in connection to simulate the frequency response of a wind farm, where FAST simulates the mechanical part of WTs and Simulink simulates the electrical part. Simulation results show that the proposed method is effective in reducing frequency drops and the impact of frequency control on the mechanical structure.
Item Type: | Conference Item (Paper) | |||||||||
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Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | |||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||
Library of Congress Subject Headings (LCSH): | Electric power systems, Offshore wind power plants, Reinforcement learning -- Mathematical models, Wind power | |||||||||
Publisher: | IEEE | |||||||||
ISBN: | 9781665408233 | |||||||||
ISSN: | 1944-9933 | |||||||||
Book Title: | 2022 IEEE Power & Energy Society General Meeting (PESGM) | |||||||||
Official Date: | 27 October 2022 | |||||||||
Dates: |
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DOI: | 10.1109/PESGM48719.2022.9917075 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Reuse Statement (publisher, data, author rights): | © 2022 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: | 7 November 2022 | |||||||||
Date of first compliant Open Access: | 8 November 2022 | |||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | |||||||||
Title of Event: | 2022 IEEE Power & Energy Society General Meeting (PESGM) | |||||||||
Type of Event: | Conference | |||||||||
Location of Event: | Denver, CO, USA | |||||||||
Date(s) of Event: | 17-21 Jul 2022 |
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