Averting Cascading Failures in Networked Infrastructures: Poset-Constrained Graph Algorithms

Cascading failures in critical networked infrastructures that result even from a single source of failure often lead to rapidly widespread outages as witnessed in the 2013 Northeast blackout in Northern America. The ensuing problem of containing future cascading failures by placement of protection o...

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Veröffentlicht in:IEEE journal of selected topics in signal processing Jg. 12; H. 4; S. 733 - 748
Hauptverfasser: Yu, Pei-Duo, Tan, Chee Wei, Fu, Hung-Lin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Approximation
approximation algorithm
Approximation algorithms
Blackout
Cascading failure
Failure
Graph algorithms
graph theory and algorithms
Inference algorithms
large-scale stochastic optimization
Mathematical analysis
Message passing
message-passing algorithms
network centrality
Power grids
Power system faults
Power system protection
Signal processing algorithms
Statistical inference
Vaccines
viral spreading
ISSN:1932-4553, 1941-0484
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Zusammenfassung:Cascading failures in critical networked infrastructures that result even from a single source of failure often lead to rapidly widespread outages as witnessed in the 2013 Northeast blackout in Northern America. The ensuing problem of containing future cascading failures by placement of protection or monitoring nodes in the network is complicated by the uncertainty of the failure source and the missing observation of how the cascading might unravel, be it the past cascading failures or the future ones. This paper examines the problem of minimizing the outage when a cascading failure from a single source occurs. A stochastic optimization problem is formulated where a limited number of protection nodes, when placed strategically in the network to mitigate systemic risk, can minimize the expected spread of cascading failure. We propose the vaccine centrality, which is a network centrality based on the partially ordered sets (poset) characteristics of the stochastic program and distributed message-passing, to design efficient approximation algorithms with provable approximation ratio guarantees. In particular, we illustrate how the vaccine centrality and the poset-constrained graph algorithms can be designed to tradeoff between complexity and optimality, as illustrated through a series of numerical experiments. This paper points toward a general framework of network centrality as statistical inference to design rigorous graph analytics for statistical problems in networks.
Bibliographie:ObjectType-Article-1
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ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2018.2844813