Cybersecurity Enhancement of Transformer Differential Protection

Abstract

Abstract: The increasing use of information and communication technologies (ICT) in the operational environments of power grids has been essential for operators to improve the monitoring, maintenance, and control of power generation, transmission, and distribution; however, this has come at the expense of increasing the grid's exposure to cyber threats. This paper looks at cyberattack scenarios that target protective relays in substations, which can be the most important part of protecting power systems from abnormal conditions. The overall performance of the power grid could suffer significantly if the relays' operations are disrupted, possibly resulting in widespread blackouts. Utilizing the potential of machine learning to detect anomalous behavior in transformer differential protective relays, we investigate methods for improving substation cybersecurity. In order to find cyberattacks, the proposed method looks at operational technology (OT) data from the substation current transformers (CTs). Power frameworks recreation utilizing OPAL-RTHYPERSIM is utilized to create preparing informational collections, to simulate the cyberattacks and to evaluate the network safety enhancement capability of the proposed AI calculations. Terms in the index include differential protective relays, transformers, operational technology, cyber physical systems, and machine learning.