Data-driven Local Control Design for Active Distribution Grids using off-line Optimal Power Flow and Machine Learning Techniques

Karagiannopoulos, S, Aristidou, P orcid.org/0000-0003-4429-0225 and Hug, G (2019) Data-driven Local Control Design for Active Distribution Grids using off-line Optimal Power Flow and Machine Learning Techniques. IEEE Transactions on Smart Grid, 10 (6). pp. 6461-6471. ISSN 1949-3053

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Copyright, Publisher and Additional Information: © 2018 IEEE. This is an author produced version of a paper published in IEEE Transactions on Smart Grid. 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. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: data-driven control design , decentralized control , active distribution networks , OPF , backward forward sweep power flow , machine learning , distributed energy resources
Dates:
  • Published: November 2019
  • Accepted: 11 March 2019
  • Published (online): 15 March 2019
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 18 Mar 2019 15:11
Last Modified: 08 Jun 2020 16:26
Published Version: https://ieeexplore.ieee.org/document/8667699
Status: Published
Publisher: Institute of Electrical and Electronics Engineers
Identification Number: https://doi.org/10.1109/TSG.2019.2905348

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