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

TL;DR

This paper introduces a joint optimization framework combining sensor placement and a physics-informed graph transformer for improved attack detection in power systems, demonstrating significant robustness and accuracy improvements.

Contribution

It presents a novel physics-informed graph transformer network and a multi-objective optimization approach for sensor placement, enhancing detection performance and robustness in smart grids.

Findings

Achieves up to 37% improvement in detection accuracy.

Reduces false alarm rate to 0.3%.

Significantly improves power system state estimation accuracy.

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 to unseen attacks and outperforms other graph network-based variants (topology-aware models), achieving improvements up to 37% in accuracy and 73% in detection rate, with a mean false alarms rate of 0.3%. In addition, optimized sensor layouts significantly improve the performance of power system state estimation, achieving a 61%--98% reduction in the average state error.