Sarah Dunn

Hazard tolerance of spatially distributed complex networks

In this paper, we present a new methodology for quantifying the reliability of complex systems, using techniques from network graph theory. In recent years, network theory has been applied to many areas of research and has allowed us to gain insight into the behaviour of real systems that would otherwise be difficult or impossible to analyse, for example increasingly complex infrastructure systems. Although this work has made great advances in understanding complex systems, the vast majority of these studies only consider a systems topological reliability and largely ignore their spatial component. It has been shown that the omission of this spatial component can have potentially devastating consequences. In this paper, we propose a number of algorithms for generating a range of synthetic spatial networks with different topological and spatial characteristics and identify real-world networks that share the same characteristics. We assess the influence of nodal location and the spatial distribution of highly connected nodes on hazard tolerance by comparing our generic networks to benchmark networks. We discuss the relevance of these findings for real world networks and show that the combination of topological and spatial configurations renders many real world networks vulnerable to certain spatial hazards.

SECURING COMMUNITY RESILIENCE BY MODERN INFRASTRUCTURE DESIGN

In the National Security Strategy and Strategic Defence and Security Review, the UK Government prioritised the need to improve security and resilience against attack, damage or destruction of infrastructure critical to keeping the country running. In response to this a guide (Keeping the Country Running: Natural Hazards & Infrastructure) was produced to focus on one of these threats, natural hazards. The purpose of the guide is outlined as “to encourage infrastructure owners and operators, emergency responders, industry groups, regulators, and government departments to work together to improve the resilience of critical infrastructure and essential services” [1]. “To share best practice and advice to enable organisations to continuously improve their infrastructure’s resilience to natural hazards.” And to “supplement existing guidance and fills gaps identified during the consultation on the Strategic Framework and Policy Statement in March 2010”. The guide suggests that resilience can be secured through a combination of four main elements; namely: resistance, reliability, redundancy, and response and recovery. In this paper we demonstrate how these components all contribute to achieving resilient communities. We analyse the resilience of a simple network which is augmented and subjected to events of different magnitudes to quantify the performance of the degraded systems. In our examples, the first network has a greater resistance (which incorporates reliability) to the disruption, whilst the second network has a greater redundancy and the third network has a superior response. We demonstrate that for each disruption the three networks have different resilience and that the most resilient network changes for each level of disruption. The examples presented in this paper illustrate a methodology for designing resilient critical infrastructure networks and demonstrate how all elements of resilience must be considered to achieve communities that can resist natural hazards.

Development of empirical vulnerability curves for electrical supply systems subjected to wind hazard

In this paper, we develop a series of empirical vulnerability curves for energy distribution infrastructure in the UK, specifically for overhead line components, when subjected to wind storm hazard. We have achieved this by combining an atmospheric model, driven by reanalysis data, with empirical fault data from 1991 to 2010. The fault data used in this study comes from a national database of electricity distribution faults. While the fault data in this database is comprehensive, it has the deficiency of not recording the exact location of the fault, instead it only indicates which District Network Operator owned the asset. Better fault location information is available, but this is only available from the Operator. We also investigate the sensitivity of vulnerability curves to three different resolutions of the fault information; namely by Operator, Region and Area in order to evaluate the impact that this has to the vulnerability curve. From the results shown in this paper, we can conclude that the spatial resolution of the hazard data can have a significant impact to the vulnerability curve, particularly for large wind storm hazards.

Fragility Curves for Assessing the Resilience of Electricity Networks Constructed from an Extensive Fault Database

AbstractRobust infrastructure networks are vital to ensure community resilience; their failure leads to severe societal disruption and they have important postdisaster functions. However, as these ...