Anomaly-Aware Smart Grid Monitoring for Early Outage Risk Assessment

Abstract

Modern smart grids are becoming increasingly complex, creating a growing need for monitoring systems capable of detecting abnormal behavior before major power failures occur. This paper presents a real-time smart grid monitoring framework for early outage-risk detection using machine learning and streaming analytics. Smart meter data are processed using temporal feature extraction, lag observations, and rolling statistical measures to capture variations in electricity consumption patterns. Since labeled outage datasets are not readily available, an Isolation Forest-based anomaly detection model is used to identify unusual load behavior. To reduce false alarms, detected anomalies are further verified through statistical threshold analysis. The framework also supports continuous monitoring through a lightweight real-time dashboard with live visualization and streaming inference. Experimental results show that the system can effectively detect abnormal load fluctuations while maintaining low computational overhead suitable for real time deployment. The proposed approach provides a scalable and practical solution for proactive smart grid monitoring and anomaly-aware outage-risk assessment.