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Power network robustness analysis based on electrical engineering and complex network theory

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  • Zhou, Dongyue
  • Hu, Funian
  • Wang, Shuliang
  • Chen, Jun

Abstract

The growing importance of power systems in the development of modern society has increasingly focused the attention on the various dangers to which these systems are exposed. This paper proposes a robust analysis framework based on complex network theory with the aim of exploring the robustness of the power system from a methodological perspective. The analysis framework establishes three models: a purely topological model, an artificial flow model, and a direct current power flow model to analyze the power system structure and functional robustness. We present different analysis metrics under different models, simulate three fault scenarios, and conduct an evaluation and analysis. The validity of the evaluation analysis was further verified by adopting IEEE300 and two randomly generated 1000-node network models that meet the characteristics of small world and scale, respectively, for detailed robustness analysis. The results show that the proposed method can effectively analyze a power system from the perspectives of pure topology, artificial flow, and direct current power flow. The case analysis based on the IEEE300 network and systems with different network characteristics proves that the framework is effective for the evaluation of power systems with different characteristics.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:564:y:2021:i:c:s0378437120308384
    DOI: 10.1016/j.physa.2020.125540
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    References listed on IDEAS

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    Cited by:

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    3. Ma, Xiangyu & Zhou, Huijie & Li, Zhiyi, 2021. "On the resilience of modern power systems: A complex network perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    4. Wang, Ziqi & Pei, Yulong & Liu, Jing & Liu, Hehang, 2023. "Vulnerability analysis of urban road networks based on traffic situation," International Journal of Critical Infrastructure Protection, Elsevier, vol. 41(C).

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