A Physics-Informed Graph Neural Network Framework for N-2 Contingency Screening: A Real-World Texas Grid Study

This paper proposes a physics-informed graph neural network (GNN) framework for scalable and efficient AC power flow-based N-2 contingency screening in large-scale power systems. Formulated as a graph classification problem, the approach is specifically designed to identify critical N-2 contingencies that are likely to result in infeasible post-contingency AC power flow solutions. The integration of physics-based domain knowledge into the neural network architecture enhances the model’s capability to capture the underlying physical behaviors governing power flow, thereby improving classification accuracy. Comprehensive numerical experiments on the real-world Texas transmission network demonstrate that the proposed method achieves a 37-fold improvement in computational efficiency over conventional simulation-based N-2 contingency analysis techniques, underscoring its potential for operational deployment in real-time or near real-time security assessment.