优化电网传感器布局与检测模型，提升攻击识别能力。
Joint Sensor Deployment and Physics-Informed Graph Transformer for Smart Grid Attack Detection
- 用遗传算法联合优化传感器位置和检测模型性能。
- 在7个测试系统中检测准确率最高提升37%，误报率仅0.3%。
- 适合电力系统安全防护与智能监控研究者参考。
本文提出一种联合多目标优化框架，用于电力系统中战略传感器部署以增强攻击检测能力。提出一种新型物理信息图变压器网络（PIGTN）检测模型。非支配排序遗传算法-II（NSGA-II）联合优化传感器位置与PIGTN的检测性能，同时考虑实际约束。利用NSGA-II探索可行传感器布置的组合空间，并在闭环设置中同步训练所提出的检测器。相比基线传感器部署方法，该框架在七组基准案例（包括14、30、IEEE-30、39、57、118及200节点系统）中均表现出对传感器故障的鲁棒性，并提升检测性能。通过引入交流功率流约束，所提PIGTN检测模型对未见攻击具有良好泛化能力，优于其他基于图网络的变体（拓扑感知模型），准确率最高提升37%，检测率最高提升73%，平均误报率为0.3%。此外，优化后的传感器布局显著改善了电力系统状态估计性能，平均状态误差降低61%至98%。
原文摘要 · Abstract (English)
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.
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