AI-Driven SCADA Grid Intelligence for Predictive Fault Detection, Cyber Health Monitoring, and Grid Reliability Enhancement

Modern electric power systems increasingly rely on Supervisory Control and Data Acquisition (SCADA) infrastructures to ensure reliable generation, transmission, and distribution of energy across geographically distributed assets. However, conventional SCADA systems remain largely reactive, offering limited predictive capability, fragmented cybersecurity monitoring, and delayed fault localization. These limitations expose critical grid infrastructure to increased downtime, cascading failures, and cyber-physical threats. This paper presents an AI-driven SCADA Grid Intelligence platform that integrates real-time telemetry ingestion, machine learning–based fault prediction, cybersecurity health monitoring, and decision-support analytics within a unified architecture. The proposed system leverages multi-modal SCADA data streams, ensemble learning models, and automated risk assessment to predict electrical faults, detect anomalous cyber-communication behavior, and optimize maintenance actions before failures occur. A fully functional prototype has been implemented and evaluated across multiple simulated and semi-realistic grid environments, including wind, solar, and hybrid energy sites. Experimental results demonstrate significant improvements in detection accuracy (up to 89% F1-score), early warning lead time (average 35 minutes), and measurable reductions in downtime and operational cost. The results validate the feasibility of AI-augmented SCADA intelligence as a scalable solution for enhancing grid resilience, cybersecurity compliance, and operational efficiency in modern power systems.