实时动态N-1筛查：在常见故障后识别高风险线路和变压器
Real-Time Dynamic N-1 Screening: Identifying High-Risk Lines and Transformers After Common Faults
AI总结：
本文提出实时动态N-1筛查方法，通过概率评估识别故障后高风险线路和变压器，提升电力系统实时监控效率。
摘要：
电力系统运营商通常进行N-1备用分析，但传统工具在快速故障后瞬态期间对哪些线路或变压器需要特别关注的指导有限。特别是静态筛查无法揭示是否（1）同一故障线路反复引发严重下游过载，或（2）特定变压器在许多不同故障场景中显得脆弱。本文介绍了一种实时动态N-1筛查框架，通过估计每个反事实单相输电故障的暂态过电流概率来解决这一差距。输出是一个面向操作员的仪表板，对（a）导致危险变压器过载最频繁的故障线路和（b）在高风险场景中持续过载的变压器进行排名，这两种都是实时态势感知的可操作指标。该方法利用线性随机形式的摆动方程对故障后机电动态进行建模，结合分析暂态评估与基于交叉熵的重要性采样来高效估计罕见但高影响事件。所有N-1备用情况均以线性计算复杂度并行评估。该框架在IEEE 118节点系统上得到验证，揭示了在确定性动态或静态N-1分析中不可见的潜在高风险线路和变压器。结果表明，与暴力蒙特卡洛方法相比，计算速度提高了多个数量级，使在实时操作周期内实际部署成为可能。
英文摘要：
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.
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