[2603.00085] Joint Sensor Deployment and Physics-Informed Graph Transformer for Smart Grid Attack Detection

Computer Science > Neural and Evolutionary Computing arXiv:2603.00085 (cs) [Submitted on 16 Feb 2026] Title:Joint Sensor Deployment and Physics-Informed Graph Transformer for Smart Grid Attack Detection Authors:Mariam Elnour, Mohammad AlShaikh Saleh, Rachad Atat, Xiang Huo, Abdulrahman Takiddin, Muhammad Ismail, Hasan Kurban, Katherine R. Davis, Erchin Serpedin View a PDF of the paper titled Joint Sensor Deployment and Physics-Informed Graph Transformer for Smart Grid Attack Detection, by Mariam Elnour and 8 other authors View PDF HTML (experimental) Abstract:This paper proposes a joint multi-objective optimization framework for strategic sensor placement in power systems to enhance attack detection. A novel physics-informed graph transformer network (PIGTN)-based detection model is proposed. Non-dominated sorting genetic algorithm-II (NSGA-II) jointly optimizes sensor locations and the PIGTN's detection performance, while considering practical constraints. The combinatorial space of feasible sensor placements is explored using NSGA-II, while concurrently training the proposed detector in a closed-loop setting. Compared to baseline sensor placement methods, the proposed framework consistently demonstrates robustness under sensor failures and improvements in detection performance in seven benchmark cases, including the 14, 30, IEEE-30, 39, 57, 118 and the 200 bus systems. By incorporating AC power flow constraints, the proposed PIGTN-based detection model generalizes well t...