Resilience-based component importance measures for critical infrastructure network systems

Yi-Ping Fang (1) , Nicola Pedroni (1) , Enrico Zio (1, 2)

1 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec
2 POLIMI - Politecnico di Milano [Milan]

In this paper, we propose two metrics, i.e. the optimal repair time and the resilience reduction worth, to measure the criticality of the components of a network system from the perspective of their contribution to system resilience. Specifically, the two metrics quantify (i) the priority with which a failed component should be repaired and re-installed into the network, and (ii) the potential loss in the optimal system resilience due to a time delay in the recovery of a failed component, respectively. Given the stochastic nature of disruptive events on infrastructure networks, a Monte Carlo-based method is proposed to generate probability distributions of the two metrics for all the components of the network; then, a stochastic ranking approach based on the Copeland's pairwise aggregation is used to rank components importance. Numerical results are obtained for the IEEE 30 Bus test network and a comparison is made with three classical centrality measures.

Mots clés: Critical Infrastructure; system resilience; component importance measures; system recovery; stochastic ranking

Domaines: Sciences de l'ingénieur [physics]; Energie électrique; Complexité [cs.CC]; Modélisation et simulation; Réseaux et télécommunications [cs.NI]; Performance et fiabilité [cs.PF]

Yi-Ping Fang, Nicola Pedroni, Enrico Zio. Resilience-based component importance measures for critical infrastructure network systems. IEEE Transactions on Reliability, 2016, 65 (2), pp.502-512. ⟨10.1109/TR.2016.2521761⟩. ⟨hal-01436576v1⟩