Event-Structured Physics-Informed Neural Networks for Differentiable Critical Clearing Boundaries

• URL: http://arxiv.org/abs/2607.27681v1
• Date: Thu, 30 Jul 2026 04:56:29 GMT
• Title: Event-Structured Physics-Informed Neural Networks for Differentiable Critical Clearing Boundaries
• Abstract summary: Transient-stability assessment determines whether a power system can recover after a disturbance.<n>Key metric is the critical clearing time (CCT), which specifies the maximum time available to clear a fault before synchronism is lost.<n>We propose an event-structured physics-informed neural network (ES-PINN) that aligns its representation with the pre-fault, fault-on, and post-clearing swing dynamics.
• Score: 4.920314418301607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transient-stability assessment determines whether a power system can recover after a disturbance and is therefore essential to preventing generator trips and cascading outages. A key metric is the critical clearing time (CCT), which specifies the maximum time available to clear a fault before synchronism is lost. Reliable CCT estimation is challenging because complicated fault-clearing dynamics require repeated simulations over many fault severities and clearing times. We propose an event-structured physics-informed neural network (ES-PINN) that aligns its representation with the pre-fault, fault-on, and post-clearing swing dynamics and enforces exact state chaining across event interfaces. A smooth trajectory-induced stability margin defines a differentiable approximation of the CCT boundary, enabling accurate boundary extraction, local sensitivity analysis, and optional direct CCT prediction through a distilled readout. We further prove a local residual-to-trajectory-to-CCT error estimate, in which exact event chaining eliminates separate state-interface defect terms. Experiments on IEEE 9-, 14-, and 30-bus systems show that ES-PINN consistently improves held-out trajectory and stability-boundary accuracy over matched neural-surrogate baselines across mechanical and electrical contingencies with multiple clearing configurations. Additional full-network DAE validation, multi-fault experiments, and runtime analyses further demonstrate the effectiveness and computational efficiency of the proposed framework.

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