Abstract
The complexity of large-scale network systems made of a large number of nonlinearly interconnected components is a restrictive facet for their modeling and analysis. In this paper, we propose a framework of hierarchical modeling of a complex network system, based on a recursive unsupervised spectral clustering method. The hierarchical model serves the purpose of facilitating the management of complexity in the analysis of real-world critical infrastructures. We exemplify this by referring to the reliability analysis of the 380 kV Italian Power Transmission Network (IPTN). In this work of analysis, the classical component Importance Measures (IMs) of reliability theory have been extended to render them compatible and applicable to a complex distributed network system. By utilizing these extended IMs, the reliability properties of the IPTN system can be evaluated in the framework of the hierarchical system model, with the aim of providing risk managers with information on the risk/safety significance of system structures and components.
Highlights
Critical infrastructures are engineered distributed systems which provide the fundamental support to modern Industry and society
We propose a framework of hierarchical modeling of a complex network system, based on a recursive unsupervised spectral clustering method
We take for exemplification of the analyses proposed the 380 kV Italian power transmission network (IPTN) (Figure 1)
Summary
Critical infrastructures are engineered distributed systems which provide the fundamental support to modern Industry and society. Hierarchically modularized organization, which is a central idea for the life process in biology [5,7], is found to characterize the internal structure of many technological networks [8] This sparks the idea of utilizing the hierarchical, modularized structure as a basis to model these complex systems, for their analysis and understanding [9].
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