Vulnerability of critical infrastructures : identifying critical nodes.

Cox, Roger Gary ; Robinson, David Gerald

Sandia National Laboratories

关键词: Computers Terrorism-Simulation Methods.;    Terrorism-Vulnerability-Analysis.;    Vulnerability;    24 Power Transmission And Distribution;    Planning;   

DOI  :  10.2172/919120
RP-ID  :  SAND2004-2696
RP-ID  :  AC04-94AL85000
RP-ID  :  919120

美国|英语

来源: UNT Digital Library

【 摘 要 】

The objective of this research was the development of tools and techniques for the identification of critical nodes within critical infrastructures. These are nodes that, if disrupted through natural events or terrorist action, would cause the most widespread, immediate damage. This research focuses on one particular element of the national infrastructure: the bulk power system. Through the identification of critical elements and the quantification of the consequences of their failure, site-specific vulnerability analyses can be focused at those locations where additional security measures could be effectively implemented. In particular, with appropriate sizing and placement within the grid, distributed generation in the form of regional power parks may reduce or even prevent the impact of widespread network power outages. Even without additional security measures, increased awareness of sensitive power grid locations can provide a basis for more effective national, state and local emergency planning. A number of methods for identifying critical nodes were investigated: small-world (or network theory), polyhedral dynamics, and an artificial intelligence-based search method - particle swarm optimization. PSO was found to be the only viable approach and was applied to a variety of industry accepted test networks to validate the ability of the approach to identify sets of critical nodes. The approach was coded in a software package called Buzzard and integrated with a traditional power flow code. A number of industry accepted test networks were employed to validate the approach. The techniques (and software) are not unique to power grid network, but could be applied to a variety of complex, interacting infrastructures.