Graph-Based Dynamic Activity Prediction for Real-Time Power System Applications

Abstract

Online dynamic security assessment and day-ahead planning require rapid identification of where disturbances will produce significant dynamic activity. Full time-domain simulations are computationally demanding, while existing machine-learning approaches predict system-level outcomes or require post-disturbance trajectories. This paper proposes a pair-aware Graph Isomorphism Network with Edge features (GINE) framework comprising two task-specific models for predicting bus-voltage and generator apparent-power activity from the operating point and contingency description. Physically aware KPI cuts convert simulated responses into ordinal activity targets for model training. Evaluations on Nordic 32 and the French transmission system with more than 6 000 buses show that pair-aware fusion and graph embeddings improve on a local baseline on Nordic 32, with GINE the lower-cost graph encoder, and that the framework remains effective at large scale. As a downstream application, the predicted activity maps guide Adaptive Model Simplification, reducing continuous and discrete model variables by 72% and 66%, achieving speedups of up to 3.16x, and preserving voltage trajectories when critical substations are retained. These results demonstrate scalable, pre-disturbance prediction of component-level dynamic activity.