  As Information and Communication Technology (ICT) equipment continues to be
integrated into power systems, issues related to cybersecurity are increasingly
emerging. Particularly noteworthy is the transition to digital substations,
which is shifting operations from traditional hardwired-based systems to
communication-based Supervisory Control and Data Acquisition (SCADA) system
operations. These changes in the power system have increased the vulnerability
of the system to cyber-attacks and emphasized its importance. This paper
proposes a machine learning (ML) based post event analysis of the power system
in order to respond to these cybersecurity issues. An artificial neural network
(ANN) and other ML models are trained using transient fault measurements and
cyber-attack data on substations. The trained models can successfully
distinguish between power system faults and cyber-attacks. Furthermore, the
results of the proposed ML-based methods can also identify 10 different fault
types and the location where the event occurred.
