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

Abstract

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.

A heuristic method to identify optimum seismic retrofit strategies for critical infrastructure systems

“…Component importance-based methods identify a set of critical components to be retrofitted in terms of their topological metrics, spatial locations, retrofit efficacy, or physical properties. These methods are frequently used and suggested in the field of network science (Cisneros-Saldana, Hosseinian, & Butenko, 2018;Dunn & Wilkinson, 2013;Scherb et al, 2017;Si, Zhao, Cai, & Dui, 2020), and have been also applied to infrastructure retrofit and hardening problems. This article selects degree, betweenness, spatial location (weighted average distance to potential epicenters), and retrofit efficacy (performance gain after retrofitting the component) as the metrics to measure component importance (Sun & Zeng, 2017;Taylor & D'Este, 2007;Ulusan & Ergun, 2018).…”

“…In this regard, improving critical infrastructure systems, such as electric power, water supply, transportation, and communication systems, has been very important due to their essential roles in the smooth functioning of modern societies. There exist many mitigation strategies proposed by researchers in the literature for infrastructure systems, including deploying backup systems (Adachi & Ellingwood, 2008), improving system topology (Dawson, Peppe, & Wang, 2011;Dueñas-Osorio, Craig, Goodno, & Bostrom, 2007;Najafi, Peiravi, & Guerrero, 2018;Rupi, Bernardi, Rossi, & Danesi, 2015), expanding system capacity (L. Chang, Peng, Ouyang, Elnashai, & Spencer, 2012;Hackl, Lam, Heitzler, Adey, & Hurni, 2018;Kouvelis & Tian, 2014;Romero, Nozick, Dobson, Xu, & Jones, 2013;Scherb, Garrè, & Straub, 2017), and retrofitting critical components (Dong, Frangopol, & Sabatino, 2015;Du & Peeta, 2014;Gomez & Baker, 2019;Kraft, Erhun, Carlson, & Rafinejad, 2013;Miller-Hooks, Zhang, & Faturechi, 2012;Panteli, Trakas, Mancarella, & Hatziargyriou, 2017;Peeta, Salman, Gunnec, & Viswanath, 2010;Romero, Nozick, Dobson, Xu, & Jones, 2015;Salman & Li, 2018;Yan et al, 2017;Zhang & Wang, 2016). Here, components refer to physical engineering facilities, which work together to provide infrastructure services, such as substations, transmission towers for electric power systems, and tanks and pipes for water supply systems.…”

A multi‐perspective framework for seismic retrofit optimization of urban infrastructure systems

“…[12][13][14][15][16][17] The first performance measure is reliability, which is defined as the probability that a network remains operative given the occurrence of a disaster or disruptive event. 18,73,74 Scherb et al 19 determined the component ranks for power systems improvement planning according to their influence on the overall system reliability. Similar reliability-based metrics were used by Vanzi 20 to identify critical components for upgrading electric power systems against earthquakes, by Espiritu et al 21 to prioritize reliability-based improvement activities for the electric power systems, and also by Rokneddin et al 22 to optimize seismic retrofit strategies for aging transportation networks.…”

“…The first performance measure is reliability, which is defined as the probability that a network remains operative given the occurrence of a disaster or disruptive event 18,73,74 . Scherb et al 19 . determined the component ranks for power systems improvement planning according to their influence on the overall system reliability.…”

Risk of Cascading Blackouts Given Correlated Component Outages

“…In [13], spatial correlation was achieved by determining outage rates of lines adjacent to initial failures probabilistically, according to a Poisson process. In [19], a random field with spatial autocorrelation was used in a cascade model to assess risk from common-cause events. Others [20] have simulated the impact of hidden relay failures on cascading failure risk by allowing proximate lines to trip probabilistically.…”