זיהוי סיכונים ואנומליות לפעולת רשת תפוצה אופטימלית בהתבסס על למידה מחדש וספירת הסתברות

תקציר מקורי באנגליתarXiv:2609.03308v1 Announce Type: new Abstract: Reliable operation of modern distribution networks requires timely identification of operational risks and anomalous events under pervasive uncertainty. In practice, operators must identify risks that are inherent in stochastic yet in-distribution conditions, and anomalies that correspond to out-of-distribution behaviors such as unusual load patterns, extreme weather or cyber-physical attacks. This paper addresses this joint risk and anomaly identification problem for optimal distribution network operation and proposes a deep reinforcement learning framework that is explicitly uncertainty aware. We integrate distributional and Bayesian deep reinforcement learning to realize a second- order uncertainty quantification scheme that decomposes tot