Vulnerability analysis and critical areas identification of the power systems under terrorist attacks

This paper takes central China power grid (CCPG) as an example, and analyzes the vulnerability of the power systems under terrorist attacks. To simulate the intelligence of terrorist attacks, a method of critical attack area identification according to community structures is introduced. Meanwhile, three types of vulnerability models and the corresponding vulnerability metrics are given for comparative analysis. On this basis, influence of terrorist attacks on different critical areas is studied. Identifying the vulnerability of different critical areas will be conducted. At the same time, vulnerabilities of critical areas under different tolerance parameters and different vulnerability models are acquired and compared. Results show that only a few number of vertex disruptions may cause some critical areas collapse completely, they can generate great performance losses the whole systems. Further more, the variation of vulnerability values under different scenarios is very large. Critical areas which can cause greater damage under terrorist attacks should be given priority of protection to reduce vulnerability. The proposed method can be applied to analyze the vulnerability of other infrastructure systems, they can help decision makers search mitigation action and optimum protection strategy.

[1]  Jianwei Wang,et al.  Attack robustness of cascading model with node weight , 2014 .

[2]  Min Ouyang,et al.  Comparisons of complex network based models and direct current power flow model to analyze power grid vulnerability under intentional attacks , 2014 .

[3]  Yun Li,et al.  Cascading load model in interdependent networks with coupled strength , 2015 .

[4]  Ian Dobson,et al.  An initial model fo complex dynamics in electric power system blackouts , 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.

[5]  Xiang-Sun Zhang,et al.  Potts model based on a Markov process computation solves the community structure problem effectively , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  M. Ouyang.  Comparisons of purely topological model, betweenness based model and direct current power flow model to analyze power grid vulnerability. , 2013, Chaos.

[7]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Min Ouyang,et al.  Review on modeling and simulation of interdependent critical infrastructure systems , 2014, Reliab. Eng. Syst. Saf..

[9]  Irene Eusgeld,et al.  Adopting HLA standard for interdependency study , 2011, Reliab. Eng. Syst. Saf..

[10]  Ian Dobson,et al.  Cascading dynamics and mitigation assessment in power system disturbances via a hidden failure model , 2005 .

[11]  Bo Xu,et al.  Abnormal Behavior in Cascading Dynamics with Node Weight , 2015, PloS one.

[12]  Shaobu Wang,et al.  Identification of Vulnerable Lines in Power Grid Based on Complex Network Theory , 2007, 2007 IEEE Power Engineering Society General Meeting.

[13]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Amir Bashan,et al.  Localized attacks on spatially embedded networks with dependencies , 2015, Scientific Reports.

[15]  Ake J Holmgren,et al.  Using Graph Models to Analyze the Vulnerability of Electric Power Networks , 2006, Risk analysis : an official publication of the Society for Risk Analysis.

[16]  Min Ouyang,et al.  Vulnerability effects of passengers' intermodal transfer distance preference and subway expansion on complementary urban public transportation systems , 2017, Reliab. Eng. Syst. Saf..

[17]  Albert-László Barabási,et al.  Universal resilience patterns in complex networks , 2016, Nature.

[18]  Qiang Qiang,et al.  A network efficiency measure for congested networks , 2007 .

[19]  Hui-Jia Li,et al.  Social significance of community structure: Statistical view , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Min Ouyang,et al.  Vulnerability assessment and mitigation for the Chinese railway system under floods , 2015, Reliab. Eng. Syst. Saf..

[21]  Weiguo Gong,et al.  Identification of vulnerable lines in power grid based on complex network theory , 2011, 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC).

[22]  Min Ouyang,et al.  Critical location identification and vulnerability analysis of interdependent infrastructure systems under spatially localized attacks , 2016, Reliab. Eng. Syst. Saf..

[23]  Min Ouyang,et al.  A three-stage resilience analysis framework for urban infrastructure systems , 2012 .

[24]  Mark Newman,et al.  Networks: An Introduction , 2010 .