面向基础设施韧性规划的持久时空停电热点检测
Persistent Geospatial Outage Scenario Construction for Interdependent Infrastructure Simulation
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中文总结 AI 辅助
该研究提出数据驱动地理空间框架，结合HPI与多尺度DBSCAN，将持久停电热点场景注入MIIM评估，发现3个集群占54.4%级联影响，HPI可优化基础设施韧性规划的保护优先级。
中文摘要 AI 辅助
极端天气事件正在美国产生持久的电网中断地理模式，但停电热点检测与基础设施级联建模通常被分开研究。本文提出一种数据驱动的地理空间框架，将持久停电脆弱性与相互依赖的电力-通信网络的下游级联影响关联起来。利用2015-2023年的全国停电数据集，我们引入了热点持久性指数（HPI），这是一种感知严重程度的指标，用于识别随时间反复成为停电热点的县。随后，我们应用多尺度DBSCAN细化程序，将持久的县级热点转化为地理上可解释的区域故障场景，这些场景具有重现性、严重程度和空间范围特征。为评估这些场景的系统级相关性，我们将这些基于经验推导的场景注入改进的关联相互依赖模型（MIIM），该模型捕获耦合电力与通信层的级联行为。结果显示，三个持久区域集群占总HPI加权级联影响的54.4%；在高持久性场景下，通信层实体的故障速率是电力母线的2.5倍。与基于度和介数中心性的基线相比，HPI指导的加固重新确定了保护候选者的优先级，识别出仅拓扑排名所忽略的高价值母线。这些结果表明，持久的地理空间停电模式如何支持有针对性且基于经验的基础设施韧性规划。
英文摘要
Extreme weather events are producing persistent geographic patterns of power-grid disruption across the United States, yet outage hotspot detection and infrastructure cascade modeling are often studied separately. This paper presents a data-driven geospatial framework that links persistent outage vulnerability with downstream cascade impact in interdependent power-communication networks. Using a national outage dataset from 2015-2023, we introduce the Hotspot Persistence Index (HPI), a severity-aware metric for identifying counties that repeatedly emerge as outage hotspots over time. We then apply a multi-scale DBSCAN refinement procedure to convert persistent county-level hotspots into geographically interpretable regional failure scenarios characterized by recurrence, severity, and spatial extent. To evaluate their system-level relevance, these empirically derived scenarios are injected into the Modified Implicative Interdependency Model (MIIM), which captures cascading behavior across coupled power and communication layers. Results show that three persistent regional clusters account for 54.4% of total HPI-weighted cascade impact, while communication-layer entities fail at 2.5X the rate of power buses under high-persistence scenarios. HPI-guided hardening reprioritizes protection candidates relative to a degree- and betweenness-centrality baseline, identifying high-value buses that topology-only rankings overlook. These results demonstrate how persistent geospatial outage patterns can support targeted and empirically grounded infrastructure resilience planning.
