A SCALABLE APPROACH TO CYBER ATTACK DETECTION AND LOCALIZATION IN ACTIVE POWER DISTRIBUTION SYSTEMS

Abstract

The increasing reliance on active power distribution systems for efficient and sustainable energy delivery has heightened their vulnerability to cyber attacks. These threats can compromise system integrity, disrupt operations, and lead to significant 
economic and societal consequences. This paper presents a scalable approach to cyber attack detection and localization tailored for 
active power distribution systems. The proposed framework integrates advanced machine learning algorithms with state 
estimation techniques to enable real-time monitoring, anomaly detection, and precise localization of cyber threats.