Component importance in infrastructure networks subject to spatially distributed hazards

Scherb, Anke; Straub, Daniel; Garrè, Luca; Yang, Yu

doi:10.1201/9781315374987-390

Cited by 1 publication

(1 citation statement)

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“…None of these studies investigates how spatial dependence influences the behavior of component importance measures in spatially distributed networks. A first approach in this direction is reported in Scherb, et al [28], where we focus on the s-t connectivity for network performance assessment. Our results obtained for the IEEE118 bus system show that the spatial dependence should be explicitly considered if the correlation length of the hazard is in the range between the lengths of individual lines to the diameter of the entire system.…”

Section: Introductionmentioning

confidence: 99%

Reliability and Component Importance in Networks Subject to Spatially Distributed Hazards Followed by Cascading Failures

Scherb

Garrè

Straub

2017

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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We investigate reliability and component importance in spatially distributed infrastructure networks subject to hazards characterized by large-scale spatial dependencies. In particular, we consider a selected IEEE benchmark power transmission system. A generic hazard model is formulated through a random field with continuously scalable spatial autocorrelation to study extrinsic common-cause-failure events such as storms or earthquakes. Network performance is described by a topological model, which accounts for cascading failures due to load redistribution after initial triggering events. Network reliability is then quantified in terms of the decrease in network efficiency and number of lost lines. Selected importance measures are calculated to rank single components according to their influence on the overall system reliability. This enables the identification of network components that have the strongest effect on system reliability. We thereby propose to distinguish component importance related to initial (triggering) failures and component importance related to cascading failures. Numerical investigations are performed for varying correlation lengths of the random field to represent different hazard characteristics. Results indicate that the spatial correlation has a discernible influence on the system reliability and component importance measures, while the component rankings are only mildly affected by the spatial correlation. We also find that the proposed component importance measures provide an efficient basis for planning network improvements.

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Section: Introductionmentioning

confidence: 99%