Physics-Informed Graph Neural Jump ODEs for Cascading Failure Prediction in Power Grids
This work addresses the challenge of real-time cascading failure prediction for power grid operators, offering a novel hybrid approach that improves over existing methods.
The paper tackles the problem of predicting cascading failures in power grids by proposing PI-GN-JODE, a model that integrates graph neural networks with physics-informed components, achieving high performance metrics such as a Precision-Recall AUC of 0.991 for edge failure detection and a coefficient of determination of 0.951 for demand-not-served regression on the IEEE 118-bus system.
Cascading failures in power grids pose severe risks to infrastructure reliability, yet real-time prediction of their progression remains an open challenge. Physics-based simulators require minutes to hours per scenario, while existing graph neural network approaches treat cascading failures as static classification tasks, ignoring temporal evolution and physical laws. This paper proposes Physics-Informed Graph Neural Jump ODEs (PI-GN-JODE), combining an edge-conditioned graph neural network encoder, a Neural ODE for continuous power redistribution, a jump process handler for discrete relay trips, and Kirchhoff-based physics regularization. The model simultaneously predicts edge and node failure probabilities, severity classification, and demand not served, while an autoregressive extension enables round-by-round temporal cascade prediction. Evaluated on the IEEE 24-bus and 118-bus systems with 20,000 scenarios each, PI-GN-JODE achieves a Precision--Recall Area Under the Curve of 0.991 for edge failure detection, 0.973 for node failure detection, and a coefficient of determination of 0.951 for demand-not-served regression on the 118-bus system, outperforming a standard graph convolutional network baseline (0.948, 0.925, and 0.912, respectively). Ablation studies reveal that the four components function synergistically, with the physics-informed loss alone contributing +9.2 points to demand-not-served regression. Performance improves when scaling to larger grids, and the architecture achieves the highest balanced accuracy (0.996) on the PowerGraph benchmark using data from a different simulation framework.