An improved model for structural vulnerability analysis of power networks

Chen, Guo, Dong, Zhao Yang, Hill, David J. and Zhang, Guo Hua (2009) An improved model for structural vulnerability analysis of power networks. Physica A: Statistical Mechanics and its Applications, 388 19: 4259-4266. doi:10.1016/j.physa.2009.06.041

Author Chen, Guo
Dong, Zhao Yang
Hill, David J.
Zhang, Guo Hua
Title An improved model for structural vulnerability analysis of power networks
Journal name Physica A: Statistical Mechanics and its Applications   Check publisher's open access policy
ISSN 0378-4371
Publication date 2009-10-01
Sub-type Article (original research)
DOI 10.1016/j.physa.2009.06.041
Open Access Status Not yet assessed
Volume 388
Issue 19
Start page 4259
End page 4266
Total pages 8
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Subject 2613 Statistics and Probability
3104 Condensed Matter Physics
Abstract Electric power networks have been studied as a typical example of real-world complex networks. Traditional models for structural vulnerability analysis appear to be all based on physical topological structure. In this paper, we depict a typical power network as a weighted graph based on electrical topology by introducing its bus admittance matrix, which embodies the important characteristics of power networks in a much more realistic structure. Furthermore, the numerical simulation for both the traditional dynamical model and the proposed electrical topological model are investigated based on the IEEE 300 bus system respectively. The comparison demonstrates that the improved model is more precise and highly efficient for the analysis of structural vulnerability of power networks.
Keyword Admittance matrix
Complex networks
Network efficiency
Power networks
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 50 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 66 times in Scopus Article | Citations
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