Real-Time Small-Signal Security Assessment of Power Systems Using an Edge-Enhanced Graph Attention Network

Conventional small-signal security assessment requires repeated power flow calculations, system linearization, and eigenvalue analysis, resulting in considerable computational burden for online applications. Moreover, existing data-driven methods generally provide insufficient representation of branch power flows, line operating states, and incomplete PMU measurements. To address these limitations, this paper proposes an edge-enhanced graph attention network (EE-GAT) for real-time small-signal security assessment. The model takes bus voltage magnitude, phase angle, active and reactive power injections, and PMU availability masks as node features, while branch active and reactive power flows and line operating states are incorporated as edge features. An edge-enhanced multi-head attention mechanism is employed to jointly learn node state and branch coupling information, and a global attention readout mechanism is used to construct the system-level representation for secure/insecure classification. Tests on the New England 39-bus and NPCC 140-bus systems show that EE-GAT achieves accuracies of 97.75% and 96.47%, respectively, outperforming XGBoost, LSTM, CNN, GCN, and conventional GAT. The proposed model also maintains superior performance under incomplete PMU measurements and requires only 0.0034 s and 0.0051 s for the corresponding test batches, demonstrating its potential for online small-signal security screening.