Extracting resilience metrics and load composition from utility data
type: CreativeWork
description: Over the years devices such as fuse cards and smart meters have been incorporated into the electric power distribution systems infrastructure to record the status of the system. These devices record the details of outages and load. This thesis shows how this utility data can be processed to offer insights into the resilience and load composition of electric power distribution systems. This thesis quantifies the resilience of power distribution systems using historical utility data. Resilience concerns the power system’s response to stress from an external disruption such as bad weather. A resilience curve describes the accumulated outages and restores that occur in the power system as time progresses in response to the disruption. Several works have used resilience curves to model the power system's response before, during, and after a disruption. We developed a method of systematically detecting and extracting resilience curves from utility data. This allows resilience events of all sizes to be analyzed. It is common to divide idealized resilience curves into distinct time-dependent phases, such as outage and restoration phases. We defined these phases for resilience curves extracted from the utility data and calculated metrics for each phase, such as restoration duration, outage duration, recovery rate, and outage duration. The resilience curves are grouped in small, medium, and large sizes to determine the characteristics of curves with similar sizes. Resilience metrics were extracted from the curves for each group size, giving the probability distribution for each metric and its mean, standard deviation, and percentiles. The quantified uncertainties in the metrics assist utilities with giving upper bound estimates on metrics such as restoration time and customers hours impacted. The extraction of resilience metrics from the resilience curve using phases does not address the issue of outages and restores overlapping in time, and in our data, these processes subst…
abstract: Over the years devices such as fuse cards and smart meters have been incorporated into the electric power distribution systems infrastructure to record the status of the system. These devices record the details of outages and load. This thesis shows how this utility data can be processed to offer insights into the resilience and load composition of electric power distribution systems. This thesis quantifies the resilience of power distribution systems using historical utility data. Resilience concerns the power system’s response to stress from an external disruption such as bad weather. A resilience curve describes the accumulated outages and restores that occur in the power system as time progresses in response to the disruption. Several works have used resilience curves to model the power system's response before, during, and after a disruption. We developed a method of systematically detecting and extracting resilience curves from utility data. This allows resilience events of all sizes to be analyzed. It is common to divide idealized resilience curves into distinct time-dependent phases, such as outage and restoration phases. We defined these phases for resilience curves extracted from the utility data and calculated metrics for each phase, such as restoration duration, outage duration, recovery rate, and outage duration. The resilience curves are grouped in small, medium, and large sizes to determine the characteristics of curves with similar sizes. Resilience metrics were extracted from the curves for each group size, giving the probability distribution for each metric and its mean, standard deviation, and percentiles. The quantified uncertainties in the metrics assist utilities with giving upper bound estimates on metrics such as restoration time and customers hours impacted. The extraction of resilience metrics from the resilience curve using phases does not address the issue of outages and restores overlapping in time, and in our data, these processes subst…
author: Carrington, Nichelle'Le
language: en
date published: 2022-05
date modified: 2026-07-24T02:38:23Z
provider: Iowa State University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Iowa State University

PropertyValue
property ID: DOI
value: 10.31274/td-20240329-334