Smart Grid Security & Efficiency: AI-Based Anomaly Detection and Theft Prevention

As energy grids evolve, ensuring their security and operational efficiency becomes critical. Thisresearch presents an AI-driven approach for detecting anomalies and preventing electricitytheft insmartgrids using data analytics and machine learning. We analyze real-world Transmission &Distribution(T&D) data from the SCADA system of Madhya Gujarat Vij Company Limited (MGVCL). Themethodology includes data preprocessing, anomaly detection, predictive analytics, andreal-timevisualization via Power BI dashboards. Additionally, smart meters integrated with IoTdevicesenhancereal-time monitoring and fraud detection. Our study demonstrates how AI-powered anomalydetectioncansignificantly improve grid reliability, security, and theft prevention strategies.