Real-Time Dynamic N-1 Screening: Identifying High-Risk Lines and Transformers After Common Faults

Abstract

Power system operators routinely perform N-1 contingency analysis, yet conventional tools provide limited guidance on which lines or transformers deserve heightened attention during fast post-fault transients. In particular, static screening does not reveal whether (1) the same faulted line repeatedly triggers severe downstream overloads, or (2) a specific transformer emerges as vulnerable across many distinct fault scenarios. This paper introduces a real-time dynamic N-1 screening framework that addresses this gap by estimating, for each counterfactual single-phase transmission fault, the probability of transient overcurrent on critical grid elements. The output is an operator-facing dashboard that ranks (a) faulted lines whose outages most frequently lead to dangerous transformer overloads, and (b) transformers that consistently overload across top-risk scenarios, both of which are actionable indicators for real-time situational awareness. The approach models post-fault electromechanical dynamics using a linear stochastic formulation of the swing equations with short-lived, fault-localized uncertainty, and combines analytic transient evaluation with cross-entropy based importance sampling to efficiently estimate rare but high-impact events. All N-1 contingencies are evaluated in parallel with linear computational complexity. The framework is demonstrated on the IEEE 118-bus system, where it reveals latent high-risk lines and transformers that remain invisible under deterministic dynamic or static N-1 analysis. Results show orders-of-magnitude computational speedup relative to brute-force Monte Carlo, enabling practical deployment within real-time operational cycles.