AI 中文总结
研究电力系统中协同隐蔽攻击检测问题，提出时间聚合攻击模型，采用核嵌入函数子空间检测方法，在改进的IEEE 14节点系统上仿真，相比传统2范数检测器，该方法检测性能更佳。
AI 中文摘要
现代电网越来越容易受到协同网络攻击，特别是能够躲避传统基于残差检测器的虚假数据注入攻击（FDIA）。虽然大多数现有检测方法依赖于瞬时测量，但协同动态攻击在引入结构化时间偏差时，在每个时间步都可能保持隐蔽。本文为多区域电力系统中的此类攻击建模和检测开发了一个联合框架。首先制定了一个时间聚合攻击模型来捕捉时间演变和区域间协调。对于检测，提出了一种核嵌入函数子空间检测（KEFSD）方法，该方法在再生核希尔伯特空间（RKHS）中对残差轨迹进行建模，并采用RKHS约束函数主成分分析（PCA）来识别异常时间模式。在改进的IEEE 14节点系统上的仿真结果表明，与传统基于残差的2范数检测器相比，该方法具有更好的检测性能。
英文摘要
Modern power grids are increasingly vulnerable to coordinated cyber-attacks, particularly false data injection attacks (FDIAs) that can evade conventional residual-based detectors. While most existing detection methods rely on instantaneous measurements, coordinated dynamic attacks can remain stealthy at each time step while introducing structured temporal deviations. This paper develops a joint framework for modeling and detecting such attacks in multi-area power systems. A time-aggregated attack model is first formulated to capture temporal evolution and inter-area coordination. For detection, a kernel-embedded functional subspace detection (KEFSD) method is proposed, which models residual trajectories in a reproducing kernel Hilbert space (RKHS) and employs RKHS-constrained functional principal component analysis (PCA) to identify anomalous temporal patterns. Simulation results on a modified IEEE 14-bus system demonstrates the proposed method achieves improved detection performance compared to the conventional residual based 2-norm detector.
