Evaluation of the robustness of critical infrastructures by Hierarchical Graph representation, clustering and Monte Carlo simulation

Ferrario, Elisa; Pedroni, Nicola; Zio, Enrico

doi:10.1016/j.ress.2016.06.007

Cited by 44 publications

(27 citation statements)

References 54 publications

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“…Thereby, hierarchical models are obtained by successive clustering of networks, i.e. decomposition of the system into different levels of details (Ferrario et al 2016).…”

Section: Traffic On Complex Networkmentioning

confidence: 99%

Estimation of traffic flow changes using networks in networks approaches

Hackl

Adey

2019

Appl Netw Sci

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Understanding traffic flow in urban areas has great importance and implications from an economic, social and environmental point of view. For this reason, numerous disciplines are working on this topic. Although complex network theory made their appearance in transportation research through empirical measures, the relationships between dynamic traffic patterns and the underlying transportation network structures have scarcely been investigated so far. In this work, a novel Networks in Networks (NiN) approach is presented to study changes in traffic flows, caused by topological changes in the transportation network. The NiN structure is a special type of multi-layer network in which vertices are networks themselves. This embedded network structure makes it possible to encode multiple pieces of information such as topology, paths, and origin-destination information, within one consistent graph structure. Since each vertex is an independent network in itself, it is possible to implement multiple diffusion processes with different physical meanings. In this way, it is possible to estimate how the travellers' paths will change and to determine the cascading effect in the network. Using the Sioux Falls benchmark network and a real-world road network in Switzerland, it is shown that NiN models capture both topological and spatial-temporal patterns in a simple representation, resulting in a better traffic flow approximation than single-layer network models.

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“…Thereby, hierarchical models are obtained by successive clustering of networks, i.e. decomposition of the system into different levels of details (Ferrario et al 2016).…”

Section: Traffic On Complex Networkmentioning

confidence: 99%

Estimation of traffic flow changes using networks in networks approaches

Hackl

Adey

2019

Appl Netw Sci

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“…A hierarchical approach to evaluating the robustness of capacitated flow networks was provided by Ferrario et al [18]. In their method, a network is represented as a multi-level flow model in which each demand node is assigned a priority level.…”

Section: Flow-based Cims Applied To Infrastructurementioning

confidence: 99%

Identifying Sensitive Components in Infrastructure Networks via Critical Flows

Williams¹

2019

Preprint

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This paper introduces a novel set of component importance measures that are based on the concept of critical flow. Various research communities have developed techniques for identifying critical components of networks. The methods in this paper extend previous work on flow-based centrality measures by incorporating insights from both critical infrastructure protection and urban planning. The motivation is to provide municipalities with a means of reasoning about the impact of urban interventions. An infrastructure system is represented as a flow network in which demand nodes are assigned both demand values and criticality ratings. Sensitive elements in the network are those that carry critical flows, where a flow is deemed critical to the extent that it satisfies critical demand. A method for computing these flows is presented, and its utility is demonstrated by comparing the new measures to existing flow centrality measures. The paper also shows how the method may be combined with a simple form of reliability analysis.

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“…A power network structure can be modelled using weighted or unweighted undirected graphs G = {N , L, w}, where N is the set of network buses (or nodes set), L is the set of lines connecting the nodes (i.e. links set) and w is the set of weights associated to the lines [10]- [20]- [21]- [22]. Generally when 130 graph-theory approaches are used, a conservative (pessimistic) hypotesis is made on the network structure, to ease the calculations.…”

Section: Background and Power Grid Modellingmentioning

confidence: 99%

Assessment of power grid vulnerabilities accounting for stochastic loads and model imprecision

Rocchetta

Patelli

2018

International Journal of Electrical Power & Energy Systems

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© 2017 Elsevier Ltd Vulnerability and robustness are major concerns for future power grids. Malicious attacks and extreme weather conditions have the potential to trigger multiple components outages, cascading failures and large blackouts. Robust contingency identification procedures are necessary to improve power grids resilience and identify critical scenarios. This paper proposes a framework for advanced uncertainty quantification and vulnerability assessment of power grids. The framework allows critical failure scenarios to be identified and overcomes the limitations of current approaches by explicitly considering aleatory and epistemic sources of uncertainty modelled using probability boxes. The different effects of stochastic fluctuation of the power demand, imprecision in power grid parameters and uncertainty in the selection of the vulnerability model have been quantified. Spectral graph metrics for vulnerability are computed using different weights and are compared to power-flow-based cascading indices in ranking N-1 line failures and random N-k lines attacks. A rank correlation test is proposed for further comparison of the vulnerability metrics. The IEEE 24 nodes reliability test power network is selected as a representative case study and a detailed discussion of the results and findings is presented

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Evaluation of the robustness of critical infrastructures by Hierarchical Graph representation, clustering and Monte Carlo simulation

Cited by 44 publications

References 54 publications

Estimation of traffic flow changes using networks in networks approaches

Estimation of traffic flow changes using networks in networks approaches

Identifying Sensitive Components in Infrastructure Networks via Critical Flows

Assessment of power grid vulnerabilities accounting for stochastic loads and model imprecision

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