  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.
