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Cybersecurity Enhancement of Transformer Differential Protection Using Machine Learning
Cybersecurity Enhancement of Transformer Differential Protection Using Machine Learning
- 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.
- Subjects :
- Computer science
business.industry
020209 energy
020208 electrical & electronic engineering
Protective relay
02 engineering and technology
Computer security
computer.software_genre
Machine learning
Current transformer
Electric power system
Electricity generation
Transmission (telecommunications)
0202 electrical engineering, electronic engineering, information engineering
Artificial intelligence
Differential (infinitesimal)
business
computer
Transformer (machine learning model)
Subjects
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