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

TL;DR

The paper tackles real-time dynamic N-1 screening to identify lines and transformers at risk during post-fault transients, addressing the insufficiency of static analyses in low-inertia grids. It develops a scalable framework based on a linear swing-equation model with fault-induced uncertainty and couples analytic transient evaluation with cross-entropy based rare-event sampling to estimate high-impact events. Key contributions include a probabilistic overload-risk metric, an operator-facing dashboard ranking top-risk contingencies, and the N1Plus engine enabling sub-second evaluation of ~10^5 fault trajectories on the IEEE 118-bus system, achieving significant speedups over brute-force Monte Carlo. The stochastic approach reveals a broader and more severe risk landscape than deterministic models, enabling timely, actionable decisions for real-time protection and planning in modern grids.

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.

Figures (5)

• Figure 1: Example of a single-phase transmission fault whose post-clearing transient leads to severe downstream stress. While the faulted line is re-energized, secondary overloads on other elements may persist --motivating the need for dynamic $N-1$ screening beyond static criteria.
• Figure 2: Conceptual Dashboard for Dynamic Contingency Screening.
• Figure 3: Fault timeline: system is balanced, pre-fault; fault is cleared, a faulty line is de-energized; line is back in service, post-fault transient.
• Figure 4: Overload evolution versus fault duration. Comparison between stochastic dynamics (SDE, left) and deterministic dynamics (ODE, right).
• Figure 5: Overload probability versus fault duration. Comparison between stochastic dynamics (SDE, left) and deterministic dynamics (ODE, right).