Jahromi, MZ, Jahromi, AA, Sanner, S et al. (2 more authors) (2020) Cybersecurity Enhancement of Transformer Differential Protection Using Machine Learning. In: 2020 IEEE Power & Energy Society General Meeting (PESGM). 2020 IEEE Power & Energy Society General Meeting (PESGM), 02-06 Aug 2020, Montreal, QC, Canada. IEEE ISBN 978-1-7281-5509-8
Abstract
The growing use of information and communication technologies (ICT) in power grid operational environments has been essential for operators to improve the monitoring, maintenance and control of power generation, transmission and distribution, however, at the expense of an increased grid exposure to cyber threats. This paper considers cyberattack scenarios targeting substation protective relays that can form the most critical ingredient for the protection of power systems against abnormal conditions. Disrupting the relays operations may yield major consequences on the overall power grid performance possibly leading to widespread blackouts. We investigate methods for the enhancement of substation cybersecurity by leveraging the potential of machine learning for the detection of transformer differential protective relays anomalous behavior. The proposed method analyses operational technology (OT) data obtained from the substation current transformers (CTs) in order to detect cyberattacks. Power systems simulation using OPAL-RT HYPERSIM is used to generate training data sets, to simulate the cyberattacks and to assess the cybersecurity enhancement capability of the proposed machine learning algorithms.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 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. |
Keywords: | Cyberphysical systems, operational technology, machine learning, differential protective relays, transformers |
Dates: |
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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: | 27 Jan 2021 13:55 |
Last Modified: | 29 Jan 2021 16:17 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/pesgm41954.2020.9282161 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:170423 |