Seismic Resilience Assessment of Electric Power Distribution Networks

Due to climate change and global warming, high-impact, low-probability events such as floods, hurricanes, and earthquakes have been observed to occur with increased intensity, posing a serious threat to the operation of critical power system infrastructures. It is imperative to enhance the resilience of the electric power distribution network (PDN) against such events. This paper aims to achieve two objectives: first, to present a new model that characterizes the risk of damage to various PDN components due to an earthquake, and second, to classify the PDN components based on their exposure to seismic stressors. Drawing on current seismology standards, this study establishes a novel fragility curve for more prevalent PDN configurations, including sectionalized single busbar, main and transfer bus, and one-and-a-half breaker arrangements. The proposed methodology accounts for significant uncertainties regarding earthquake magnitude, its epicenter location, individual equipment impacts, and the overall performance of the PDN. As a result, a risk map is produced that outlines the infrastructure at risk in the event of an earthquake. Additionally, a new resilience metric is introduced to quantify the PDN's capacity to withstand seismic forces, enabling the quantification of the PDN's performance during seismic events.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Power Delivery
Published
2024-11-19
DOI
https://doi.org/10.1109/tpwrd.2024.3502211
Citations
4
Primary Topic
Smart Grid Security and Resilience
Type
article
Field-Weighted Citation Impact
1.31
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Seismic Resilience Assessment of Electric Power Distribution Networks

Moein Moeini‐Aghtaie, Mahmud Fotuhi‐Firuzabad, Farshad Amani, Payman Dehghanian
4 citations
IEEE Transactions on Power Delivery
Smart Grid Security and Resilience
1.31
article

Seismic Resilience Assessment of Electric Power Distribution Networks

Moein Moeini‐Aghtaie, Mahmud Fotuhi‐Firuzabad, Farshad Amani, Payman Dehghanian
article en
4 citations

Abstract

Due to climate change and global warming, high-impact, low-probability events such as floods, hurricanes, and earthquakes have been observed to occur with increased intensity, posing a serious threat to the operation of critical power system infrastructures. It is imperative to enhance the resilience of the electric power distribution network (PDN) against such events. This paper aims to achieve two objectives: first, to present a new model that characterizes the risk of damage to various PDN components due to an earthquake, and second, to classify the PDN components based on their exposure to seismic stressors. Drawing on current seismology standards, this study establishes a novel fragility curve for more prevalent PDN configurations, including sectionalized single busbar, main and transfer bus, and one-and-a-half breaker arrangements. The proposed methodology accounts for significant uncertainties regarding earthquake magnitude, its epicenter location, individual equipment impacts, and the overall performance of the PDN. As a result, a risk map is produced that outlines the infrastructure at risk in the event of an earthquake. Additionally, a new resilience metric is introduced to quantify the PDN's capacity to withstand seismic forces, enabling the quantification of the PDN's performance during seismic events.

IEEE Transactions on Power DeliveryVol. 40(1)
Sharif University of Technology (IR), George Washington University (US)
Affordable and clean energy
Openalex Percentile: Top 19%
Smart Grid Security and Resilience
1.31
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.