Topological performance measures as surrogates for physical flow models for risk and vulnerability analysis for electric power systems
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<subfield code="a">Topological performance measures as surrogates for physical flow models for risk and vulnerability analysis for electric power systems</subfield>
<subfield code="c">Sarah LaRocca...[et.al]</subfield>
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<subfield code="a">Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist that can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. In this article, we suggest a classification of these models, which span from simple topologically-oriented models to advanced physical-flow-based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this article is to compare performance estimates achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combinations of models to use when analyzing large-scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed.</subfield>
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<subfield code="w">MAP20077000345</subfield>
<subfield code="t">Risk analysis : an international journal</subfield>
<subfield code="d">McLean, Virginia : Society for Risk Analysis, 1987-2015</subfield>
<subfield code="x">0272-4332</subfield>
<subfield code="g">06/04/2015 Volumen 35 Número 4 - abril 2015 </subfield>
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