AI 中文总结
针对极端天气给配电系统带来的不确定性和弹性挑战，提出事件条件不确定性建模和拓扑感知影响传播框架，集成多种分析评估方法，通过案例研究和平台可视化，可区分影响因素并支持预警评估。
AI 中文摘要
极端天气事件和分布式能源资源（DER）的日益整合给配电系统带来了越来越多的不确定性和弹性挑战。与传统确定性突发事件不同，天气驱动的中断具有概率性和时空特征，停电后果取决于地理暴露和馈线拓扑。现有方法通常侧重于确定性停电分析，而在预测不确定性下的拓扑感知运行影响评估仍然有限。本文针对暴雨事件下的配电系统，提出了一种事件条件不确定性建模和拓扑感知影响传播框架。该框架在统一工作流程中集成了概率事件跟踪建模、分支级故障筛选、下游影响传播分析和运行影响评估。最后，对IEEE 33母线配电馈线的案例研究表明，该框架可以区分地理暴露和拓扑相关的运行影响，并支持不确定场景下的渐进式早期预警影响评估。此外，在CURENT大规模测试平台（LTB）-AGVis平台上对影响区域进行了可视化。
英文摘要
Extreme weather events and the increasing integration of distributed energy resources (DERs) introduce growing uncertainty and resilience challenges for distribution systems. Unlike conventional deterministic contingencies, weather-driven disruptions exhibit probabilistic and spatial-temporal characteristics, where outage consequences depend on both geographic exposure and feeder topology. Existing approaches commonly focus on deterministic outage analysis, while topology-aware operational impact assessment under forecast uncertainty remains limited. This paper proposes an event-conditioned uncertainty modeling and topology-aware impact propagation framework for distribution systems under torrential rain events. The proposed framework integrates probabilistic event-track modeling, branch-level fault screening, downstream impact propagation analysis, and operational impact assessment within a unified workflow. Finally, case studies on the IEEE 33-bus distribution feeder demonstrate that the proposed framework can distinguish geographic exposure from topology-dependent operational impacts and support progressive early-warning impact assessment under uncertain scenarios. Furthermore, the impact zones are visualized on the CURENT Large-scale Testbed (LTB)-AGVis platform.
Comments6 pages, 8 figures, 2 tables. This paper has been accepted for presentation at the 2026 IEEE North American Power Symposium (NAPS 2026). The final version will appear in IEEE Xplore
