Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependency

Increased coupling between critical infrastructure networks, such as power and communication systems, will have important implications for the reliability and security of these systems. To understand the effects of power-communication coupling, several have studied interdependent network models and reported that increased coupling can increase system vulnerability. However, these results come from models that have substantially different mechanisms of cascading, relative to those found in actual power and communication networks. This paper reports on two sets of experiments that compare the network vulnerability implications resulting from simple topological models and models that more accurately capture the dynamics of cascading in power systems. First, we compare a simple model of topological contagion to a model of cascading in power systems and find that the power grid shows a much higher level of vulnerability, relative to the contagion model. Second, we compare a model of topological cascades in coupled networks to three different physics-based models of power grids coupled to communication networks. Again, the more accurate models suggest very different conclusions. In all but the most extreme case, the physics-based power grid models indicate that increased power-communication coupling decreases vulnerability. This is opposite from what one would conclude from the coupled topological model, in which zero coupling is optimal. Finally, an extreme case in which communication failures immediately cause grid failures, suggests that if systems are poorly designed, increased coupling can be harmful. Together these results suggest design strategies for reducing the risk of cascades in interdependent infrastructure systems

HETEROGENEOUS DATA AND PROBABILISTIC SYSTEM MODEL ANALYSES FOR ENHANCED SITUATIONAL AWARENESS AND RESILIENCE OF CRITICAL INFRASTRUCTURE SYSTEMS

The protection and resilience of critical infrastructure systems (CIS) are essential for public safety in daily operations and times of crisis and for community preparedness to hazard events. Increasing situational awareness and resilience of CIS includes both comprehensive monitoring of CIS and their surroundings, as well as evaluating CIS behaviors in changing conditions and with different system configurations. Two frameworks for increasing the monitoring capabilities of CIS are presented. The proposed frameworks are (1) a process for classifying social media big data for monitoring CIS and hazard events and (2) a framework for integrating heterogeneous data sources, including social media, using Bayesian inference to update prior probabilities of event occurrence. Applications of both frameworks are presented, including building and evaluating text-based machine learning classifiers for identifying CIS damages and integrating disparate data sources to estimate hazards and CIS damages. Probabilistic analyses of CIS vulnerabilities with varying system parameters and topologies are also presented. In a water network, the impact of varying parameters on component performance is evaluated. In multiple, small-size water networks, the impacts of system topology are assessed to identify characteristics of more resilient networks. This body of work contributes insights and methods for monitoring CIS and assessing their performance. Integrating heterogeneous data sources increases situational awareness of CIS, especially during or after failure events, and evaluating the sensitivity of CIS outcomes to changes in the network facilitates decisions for CIS investments and emergency response.Ph.D

Spatial and performance optimality in power distribution networks

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Complex network theory has been widely used in vulnerability analysis of power networks, especially for power transmission ones. With the development of the smart grid concept, power distribution networks are becoming increasingly relevant. In this paper, we model power distribution systems as spatial networks. Topological and spatial properties of 14 European power distribution networks are analyzed, together with the relationship between geographical constraints and performance optimization, taking into account economic and vulnerability issues. Supported by empirical reliability data, our results suggest that power distribution networks are influenced by spatial constraints which clearly affect their overall performance.Peer ReviewedPostprint (author's final draft

Robustness and edge addition strategy of air transport networks : a case study of 'the Belt and Road'

Air transportation is of great importance in "the Belt and Road" (the B&R) region. The achievement of the B&R initiative relies on the availability, reliability, and safety of air transport infrastructure. A fundamental step is to find the critical elements in network performance. Considering the uneven distributions of population and economy, the current literature focusing on centrality measures in unweighted networks is not sufficient in the B&R region. By differentiating power and centrality in the B&R region, our analysis leads to two conclusions: (1) Deactivating powerful nodes causes a larger decrease in efficiency than deactivating central nodes. This indicates that powerful nodes in the B&R region are more critical than central nodes for network robustness. (2) Strategically adding edges between high powerful and low powerful nodes can enhance the network's ability to exchange resources efficiently. These findings can be used to adjust government policies for air transport configuration to achieve the best network performance and the most cost effective

Infastructure Interdependencies Modeling and Analysis - A Review and Synthesis

The events of 9/11 and the occurrence of major natural disasters in recent years has resulted in increased awareness and renewed desire to protect critical infrastructure that are the pillars to maintaining what has become normal life in our economy. The problem has been compounded because the increased connectedness between the various sectors of the economy has resulted in interdependencies that allow for problems and issues with one infrastructure to affect other infrastructures. This area is now being investigated extensively after the Department of Homeland Security (DHS) prioritized this issue. There is now a vast extant of literature in the area of infrastructure interdependencies and the modeling of it. This paper presents a synthesis and survey of the literature in the area of infrastructure interdependency modeling methods and proposes a framework for classification of these studies. The framework classifies infrastructure interdependency modeling and analysis methods into four quadrants in terms of system complexities and risks. The directions of future research are also discussed in this paper