Graph-based Cascading Impact Estimation for Identifying Crucial Infrastructure Components

Sangkeun Lee, Supriya Chintavali, Sarah Tennille, Junghoon Chae, Anika Tabassum, Varisara Tansakul, Daniel Redmon, Robert Moncrief, Aaron Myers

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Critical Infrastructures (CIs) such as energy, communication, and transportation compose a complex network that sustains day-to-day commodity flows vital to national security, economic stability, and public safety. Failures caused by an extreme weather event or a man-made incident can trigger widespread cascading failures, sending ripple effects at regional or even national scales. To minimize such impact, emergency responders must identify crucial components within CIs during such stressor events in a systematic and quantifiable manner and take appropriate mitigating actions. Oak Ridge National Laboratory (ORNL) has developed a graph-based analytic system named URBAN-NET, which estimates cascading impact caused by the disruption of critical infrastructure components by leveraging the topology of a critical infrastructure network. Before and during critical events (e.g., hurricanes), the URBAN-NET system generates reports that contain the ranking of the most crucial energy components that have the most downstream impact across infrastructure layers. The developed system has been integrated with the Environment for Analysis of Geo-Located Energy Information (EAGLE-I™) system, which is a situational-awareness system operated by ORNL for the department of energy of the United States.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
EditorsShusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6749-6751
Number of pages3
ISBN (Electronic)9781665480451
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan
Duration: Dec 17 2022Dec 20 2022

Publication series

NameProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference2022 IEEE International Conference on Big Data, Big Data 2022
Country/TerritoryJapan
CityOsaka
Period12/17/2212/20/22

Funding

This manuscript has been authored by UT-Battelle LLC under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes.DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

FundersFunder number
U.S. Department of Energy
UT-BattelleDE-AC05-00OR22725

    Keywords

    • electric grid
    • extreme weather
    • machine learning
    • outages
    • winter storms

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