Big Data Intelligence for Stealthy Anomaly Detection and Mitigation in Substations to Secure Smart Grid.

The dataset captures real-time operations of the key power systems monitoring devices in a medium voltage (20 kV) outdoor substation located in a remote area.

• The datasets provide continuous monitoring of three-phase current and voltage signals, power quality indicators, and circuit breaker status using IEC 61850 (GOOSE and sampled measured values) and IEC 104/ IEEE 802.3 communication protocols.
• The datasets are highly valuable for designing, developing, and testing AI-based hybrid models and algorithms (e.g., the HML) for stealthy anomaly detection, classification, and predictive maintenance in substations and smart grids.
• The time series data represents substation behaviour under realistic normal and anomalous scenarios, and could enable utilities to evaluate anomaly detection systems, intrusion detection mechanisms, and potential defence responses in substations to safeguard smart grids.
• Various stakeholders, including grid operators, researchers, cybersecurity analysts, SCADA developers, policymakers, and planners, can benefit from the datasets to evaluate stealthy vulnerabilities, network resilience, and to design digital twin and cyber-physical defence strategies in SGs.