Invulnerability of power grids based on maximum flow theory
Wenli Fan,
Shaowei Huang and
Shengwei Mei
Physica A: Statistical Mechanics and its Applications, 2016, vol. 462, issue C, 977-985
Abstract:
The invulnerability analysis against cascades is of great significance in evaluating the reliability of power systems. In this paper, we propose a novel cascading failure model based on the maximum flow theory to analyze the invulnerability of power grids. In the model, node initial loads are built on the feasible flows of nodes with a tunable parameter γ used to control the initial node load distribution. The simulation results show that both the invulnerability against cascades and the tolerance parameter threshold αT are affected by node load distribution greatly. As γ grows, the invulnerability shows the distinct change rules under different attack strategies and different tolerance parameters α respectively. These results are useful in power grid planning and cascading failure prevention.
Keywords: Invulnerability; Cascading failures; Maximum flow theory; Complex networks (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437116303843
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:462:y:2016:i:c:p:977-985
DOI: 10.1016/j.physa.2016.06.109
Access Statistics for this article
Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis
More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().