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Cybersecurity Enhancement of Transformer Differential Protection Using Machine Learning

Cybersecurity Enhancement of Transformer Differential Protection Using Machine Learning

Authors :
Martiya Zare Jahromi
Marthe Kassouf
Amir Abiri Jahromi
Scott Sanner
Deepa Kundur
Source :
2020 IEEE Power & Energy Society General Meeting (PESGM).
Publication Year :
2020
Publisher :
IEEE, 2020.

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.

Details

Database :
OpenAIRE
Journal :
2020 IEEE Power & Energy Society General Meeting (PESGM)
Accession number :
edsair.doi...........6004aeb7cd3b4a59dd05dcb1244d4be8
Full Text :
https://doi.org/10.1109/pesgm41954.2020.9282161