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

Nawaraj Kumar Mahato, Junfeng Yang, Jiaxuan Yang, Gangjun Gong (), Jianhong Hao, Jing Sun and Jinlu Liu
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Nawaraj Kumar Mahato: Beijing Engineering Research Center of Energy Electric Power Information Security, North China Electric Power University, Beijing 102206, China
Junfeng Yang: Beijing Engineering Research Center of Energy Electric Power Information Security, North China Electric Power University, Beijing 102206, China
Jiaxuan Yang: Beijing Engineering Research Center of Energy Electric Power Information Security, North China Electric Power University, Beijing 102206, China
Gangjun Gong: Beijing Engineering Research Center of Energy Electric Power Information Security, North China Electric Power University, Beijing 102206, China
Jianhong Hao: School of Electrical and Electronics Engineering, North China Electric Power University, Beijing 102206, China
Jing Sun: Power China Northwest Engineering Corporation Limited, Xian 710065, China
Jinlu Liu: Power China Northwest Engineering Corporation Limited, Xian 710065, China

Energies, 2025, vol. 18, issue 12, 1-19

Abstract: 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.

Keywords: electric power substation physical security; Markov Decision Process; Enhanced-Dueling Deep Q-Network; threat prevention (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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