GNN-Enhanced Fault Diagnosis Method for Parallel Cyber-physical Attacks in Power Grids

Parallel cyber-physical attacks (PCPA) simultaneously damage physical transmission lines and block measurement data transmission in power grids, impairing or delaying system protection and recovery. This paper investigates the fault diagnosis problem for a linearized (DC) power flow model under PCPA. The physical attack mechanism includes not only line disconnection but also admittance modification, for example via compromised distributed flexible AC transmission system (D-FACTS) devices. To address this problem, we propose a fault diagnosis framework based on meta-mixed-integer programming (MMIP), integrating graph attention network-based fault localization (GAT-FL). First, we derive measurement reconstruction conditions that allow reconstructing unknown measurements in attacked areas from available measurements and the system topology. Based on these conditions, we formulate the diagnosis task as an MMIP model. The GAT-FL predicts a probability distribution over potential physical attacks, which is then incorporated as objective coefficients in the MMIP. Solving the MMIP yields optimal attack location and magnitude estimates, from which the system states are also reconstructed. Experimental simulations are conducted on IEEE 30/118 bus standard test cases to demonstrate the effectiveness of the proposed fault diagnosis algorithms.