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Invulnerability of power grids based on maximum flow theory

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  • Fan, Wenli
  • Huang, Shaowei
  • Mei, Shengwei

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.

Suggested Citation

  • Fan, Wenli & Huang, Shaowei & Mei, Shengwei, 2016. "Invulnerability of power grids based on maximum flow theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 977-985.
    • Handle: RePEc:eee:phsmap:v:462:y:2016:i:c:p:977-985
    DOI: 10.1016/j.physa.2016.06.109
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    References listed on IDEAS

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    Cited by:

    1. Abedi, Amin & Gaudard, Ludovic & Romerio, Franco, 2019. "Review of major approaches to analyze vulnerability in power system," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 153-172.
    2. Xia Cao & Chuanyun Li & Wei Chen & Jinqiu Li & Chaoran Lin, 2020. "Research on the invulnerability and optimization of the technical cooperation innovation network based on the patent perspective—A case study of new energy vehicles," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-19, September.
    3. Zhou, Dongyue & Hu, Funian & Wang, Shuliang & Chen, Jun, 2021. "Power network robustness analysis based on electrical engineering and complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).

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