📄 中文摘要
该研究提出了一种联合多目标优化框架，用于在电力系统中进行战略传感器布置，以增强攻击检测能力。提出了一种新颖的基于物理信息图变换器网络（PIGTN）的检测模型。采用非支配排序遗传算法-II（NSGA-II）共同优化传感器位置和PIGTN的检测性能，同时考虑实际约束条件。通过NSGA-II探索可行传感器布置的组合空间，并在闭环设置中同时训练所提出的检测器。与基线传感器布置方法相比，该框架在传感器故障情况下表现出一致的鲁棒性，并在七个基准测试中显示出检测性能的提升。
智能电网攻击检测的联合传感器部署与物理信息图变换器
📄 English Summary
Joint Sensor Deployment and Physics-Informed Graph Transformer for Smart Grid Attack Detection
This research proposes a joint multi-objective optimization framework for strategic sensor placement in power systems to enhance attack detection capabilities. A novel physics-informed graph transformer network (PIGTN)-based detection model is introduced. The non-dominated sorting genetic algorithm-II (NSGA-II) is employed to jointly optimize sensor locations and the detection performance of the PIGTN while considering practical constraints. The combinatorial space of feasible sensor placements is explored using NSGA-II, 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 across seven benchmark tests.