Machine Learning Based Cyber System Restoration for IEC 61850 Based Digital Substations

TL;DR

This paper presents an ML-based approach for detecting and mitigating cyberattacks in IEC 61850 digital substations, enhancing system resilience through anomaly detection and automated restoration strategies.

Contribution

It introduces a novel ML model trained on substation data for cyberattack detection and integrates CIEDs for automated mitigation and system restoration.

Findings

ML model accurately classifies cyberattacks and faults

Effective mitigation and restoration using CIEDs

Enhanced security in IEC 61850 substations

Abstract

Substation Automation Systems (SAS) that adhere to the International Electrotechnical Commission (IEC) 61850 standard have already been widely implemented across various on-site local substations. However, the digitalization of substations, which involves the use of cyber system, inherently increases their vulnerability to cyberattacks. This paper proposes the detection of cyberattacks through an anomaly-based approach utilizing Machine Learning (ML) methods within central control systems of the power system network. Furthermore, when an anomaly is identified, mitigation and restoration strategies employing concurrent Intelligent Electronic Devices (CIEDs) are utilized to ensure robust substation automation system operations. The proposed ML model is trained using Sampled Value (SV) and Generic Object Oriented Substation Event (GOOSE) data from each substation within the entire transmission system. As a result, the trained ML models can classify cyberattacks and normal faults, while the use of CIEDs contributes to cyberattack mitigation, and substation restoration.