Enhanced-Dueling Deep Q-Network for Trustworthy Physical Security of Electric Power Substations

This paper introduces an Enhanced-Dueling Deep Q-Network (EDDQN) specifically designed to bolster the physical security of electric power substations. We model the intricate substation security challenge as a Markov Decision Process (MDP), segmenting the facility into three zones, each with potential normal, suspicious, or attacked states. The EDDQN agent learns to strategically select security actions, aiming for optimal threat prevention while minimizing disruptive errors and false alarms. This methodology integrates Double DQN for stable learning, Prioritized Experience Replay (PER) to accelerate the learning process, and a sophisticated neural network architecture tailored to the complexities of multi-zone substation environments. Empirical evaluation using synthetic data derived from historical incident patterns demonstrates the significant advantages of EDDQN over other standard DQN variations, yielding an average reward of 7.5, a threat prevention success rate of 91.1%, and a notably low false alarm rate of 0.5%. The learned action policy exhibits a proactive security posture, establishing EDDQN as a promising and reliable intelligent solution for enhancing the physical resilience of power substations against evolving threats. This research directly addresses the critical need for adaptable and intelligent security mechanisms within the electric power infrastructure.