Centralized Dynamic State Estimation Algorithm for Detecting and Distinguishing Faults and Cyber Attacks in Power Systems

As power systems evolve with increased integration of renewable energy sources and computer-based protection and control systems, they become more vulnerable to both cyber and physical threats. This study validates a centralized Dynamic State Estimation (DSE) algorithm designed to enhance the protection of power systems, particularly focusing on microgrids with substantial renewable sources integration. The algorithm utilizes a structured hypothesis testing framework, systematically identifies and differentiates anomalies caused by cyberattacks from those resulting from physical faults. This algorithm was evaluated through four case studies: a False Data Injection Attack (FDIA) via manipulation of Current Transformer (CT) ratios, a single line-to-ground (SLG) fault, and two combined scenarios involving both anomalies. Results from real-time simulations demonstrate that the algorithm effectively distinguishes between cyber-induced anomalies and physical faults, thereby significantly enhancing the reliability and security of energy systems. This research underscores the critical role of advanced diagnostic tools for protection and control systems against the growing prevalence of cyber-physical threats, enhancing the resilience of the grid and preventing potential blackouts by avoiding mis-operation of protective relays.