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Failure Localization in Power Systems via Tree Partitions

Guo, Linqi and Liang, Chen and Zocca, Alessandro and Low, Steven H. and Wierman, Adam (2018) Failure Localization in Power Systems via Tree Partitions. Performance Evaluation Review, 46 (2). pp. 57-61. ISSN 0163-5999. https://resolver.caltech.edu/CaltechAUTHORS:20190128-094206311

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Abstract

Cascading failures in power systems propagate non-locally, making the control and mitigation of outages extremely hard. In this work, we use the emerging concept of the tree partition of transmission networks to provide an analytical characterization of line failure localizability in transmission systems. Our results rigorously establish the well perceived intuition in power community that failures cannot cross bridges, and reveal a finer-grained concept that encodes more precise information on failure propagations within tree-partition regions. Specifically, when a non-bridge line is tripped, the impact of this failure only propagates within well-defined components, which we refer to as cells, of the tree partition defined by the bridges. In contrast, when a bridge line is tripped, the impact of this failure propagates globally across the network, affecting the power flow on all remaining transmission lines. This characterization suggests that it is possible to improve the system robustness by temporarily switching off certain transmission lines, so as to create more, smaller components in the tree partition; thus spatially localizing line failures and making the grid less vulnerable to large-scale outages. We illustrate this approach using the IEEE 118-bus test system and demonstrate that switching off a negligible portion of transmission lines allows the impact of line failures to be significantly more localized without substantial changes in line congestion.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1145/3305218.3305240DOIArticle
https://arxiv.org/abs/1803.08551arXivDiscussion Paper
ORCID:
AuthorORCID
Zocca, Alessandro0000-0001-6585-4785
Additional Information:© 2018 is held by author/owner(s). This work has been supported by Resnick Fellowship, Linde Institute Research Award, NWO Rubicon grant 680.50.1529., NSF grants through PFI:AIR-TT award 1602119, EPCN 1619352, CNS 1545096, CCF 1637598, ECCS 1619352, CNS 1518941, CPS 154471, AitF 1637598, ARPA-E grant through award DE-AR0000699 (NODES) and GRID DATA, DTRA through grant HDTRA 1-15-1-0003 and Skoltech through collaboration agreement 1075-MRA.
Group:Resnick Sustainability Institute
Funders:
Funding AgencyGrant Number
Resnick Sustainability InstituteUNSPECIFIED
Ronald And Maxine Linde Institute for Economic and Management Sciences, CaltechUNSPECIFIED
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO)680.50.1529
NSFIIP-1602119
NSFEPCN-1619352
NSFCNS-1545096
NSFCCF-1637598
NSFECCS-1619352
NSFCNS-1518941
NSFCPS-154471
NSFCCF-1637598
Advanced Research Projects Agency-Energy (ARPA-E)DE-AR0000699
Defense Threat Reduction Agency (DTRA)HDTRA 1-15-1-0003
Skoltech1075-MRA
Issue or Number:2
Record Number:CaltechAUTHORS:20190128-094206311
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190128-094206311
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:92490
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:29 Jan 2019 20:02
Last Modified:03 Oct 2019 20:45

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