Unraveling Fundamental Properties of Power System Resilience Curves using Unsupervised Machine Learning

TL;DR

This study uses unsupervised machine learning to analyze over 200 power outage resilience curves from extreme weather events, revealing two main archetypes and fundamental properties that enhance understanding of power system resilience.

Contribution

It introduces a data-driven approach to classify and understand power system resilience curves, identifying key archetypes and properties beyond traditional models.

Findings

Two primary archetypes: triangular and trapezoidal curves.

Triangular curves relate to threshold, recovery rate, and pivot point.

Trapezoidal curves relate to duration of function loss and recovery rate.

Abstract

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.