Parallel cyber--physical attacks (PCPA) can simultaneously damage physical transmission lines and disrupt measurement data transmission in power grids, severely impairing system situational awareness and attack diagnosis. This paper investigates the attack diagnosis problem for linearized AC/DC power flow models under PCPA, where physical attacks include not only line disconnections but also admittance modifications, such as those caused by compromised distributed flexible AC transmission system (D-FACTS) devices. To address this challenge, we propose a learning-assisted attack diagnosis framework based on meta--mixed-integer programming (MMIP), which integrates a convolutional graph cross-attention attack localization (CGCA-AL) model. First, sufficient conditions for measurement reconstruction are derived, enabling the recovery of unknown measurements in attacked areas using available measurements and network topology information. Based on these conditions, the attack diagnosis problem is formulated as an MMIP model. The proposed CGCA-AL employs a multi-scale attention mechanism to predict a probability distribution over potential physical attack locations, which is incorporated into the MMIP as informative objective coefficients. By solving the resulting MMIP, both the locations and magnitudes of physical attacks are optimally estimated, and system states are subsequently reconstructed. Simulation results on IEEE 30-bus and IEEE 118-bus test systems demonstrate the effectiveness, robustness, and scalability of the proposed attack diagnosis framework under complex PCPA scenarios.

翻译：并行网络物理攻击（PCPA）可同时破坏物理输电线路并干扰电力系统中的测量数据传输，严重损害系统态势感知与攻击诊断能力。本文研究了PCPA下线性化交直流潮流模型的攻击诊断问题，其中物理攻击不仅包括线路断开，还包括导纳修改（例如由受损的分布式柔性交流输电系统（D-FACTS）设备引起）。为应对这一挑战，我们提出了一种基于元混合整数规划（MMIP）的学习辅助攻击诊断框架，该框架集成了卷积图交叉注意力攻击定位（CGCA-AL）模型。首先，推导了测量重构的充分条件，使得能够利用可用测量值和网络拓扑信息恢复受攻击区域的未知测量值。基于这些条件，将攻击诊断问题构建为一个MMIP模型。所提出的CGCA-AL采用多尺度注意力机制来预测潜在物理攻击位置的概率分布，该分布作为信息化的目标系数被纳入MMIP中。通过求解所得的MMIP，可最优估计物理攻击的位置和幅度，并随后重构系统状态。在IEEE 30节点和IEEE 118节点测试系统上的仿真结果表明，所提出的攻击诊断框架在复杂PCPA场景下具有有效性、鲁棒性和可扩展性。