Physics-Constrained LSTM for Cascading Failure Evolution Path Prediction in Power Systems

As the construction of new-type power systems continues, the extensive integration of renewable energy and power-electronic devices has significantly increased the risk of cascading failures in power grids, making accurate prediction of cascading-failure evolution paths crucial for grid security.However, real-world fault samples are extremely scarce, and conventional physics-based simulations are too computationally intensive for real-time online early warning. To address these challenges, this paper proposes a cascading failure evolution path prediction method based on massive event chain mining and prior knowledge constraints. First, we construct a refined cascading-failure simulation model that incorporates the action logic of the three defense lines to generate a large standardized event-chain dataset, thereby addressing the data scarcity faced by data-driven models. Second, a sequence-prediction engine that combines word embeddings, LSTM-based temporal modeling, and prior-knowledge constraints is developed; the physical action logic of the power system is explicitly incorporated into the loss function to improve the physical plausibility of the predictions. Finally, an evaluation framework from event prediction to third-defense-line early warning is constructed to assess system-level security risks. Based on simulations of the IEEE 39-bus AC/DC hybrid system, the proposed method is shown to achieve single-step Top-1 accuracies of 97.0% and 96.8% for the system-level critical events of frequency limit violation and system instability, respectively, with corresponding Top-3 accuracies of 99.2% and 99.4%. The overall Top-1 and Top-3 accuracies are 89.8% and 98.2%, respectively; the mean single-inference time is approximately 9.6 ms, corresponding to a speedup of more than 4700 times over conventional time-domain simulation, and the weighted mean early-warning lead time is 1.78 s.