The standard model of infrastructure resilience, the resilience triangle, has been the primary way of characterizing and quantifying infrastructure resilience. However, the theoretical model merely provides a one-size-fits-all framework for all infrastructure systems. Most of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system performance. Limited empirical studies hindered our ability to fully understand and predict resilience characteristics in infrastructure systems. To address this gap, this study examined over 200 resilience curves related to power outages in three major extreme weather events. Using unsupervised machine learning, we examined different curve archetypes, as well as the fundamental properties of each resilience curve archetype. The results show two primary archetypes for power system resilience curves, triangular, and trapezoidal curves. Triangular curves characterize resilience behavior based on 1. critical functionality threshold, 2. critical functionality recovery rate, and 3. recovery pivot point. Trapezoidal archetypes explain resilience curves based on 1. duration of sustained function loss and 2. constant recovery rate. The longer the duration of sustained function loss, the slower the constant rate of recovery. The findings of this study provide novel perspectives enabling better understanding and prediction of resilience performance of power system infrastructures.

通过对三次极端天气事件中与电力故障相关的超过200个恢复曲线的研究，使用无监督机器学习方法鉴别了两种电力系统恢复曲线原型，分别是三角形曲线和梯形曲线，这些结果为更好地理解和预测电力系统基础设施的恢复性能提供了新的视角。

揭示电力系统弹性曲线的基本特性：无监督机器学习的应用