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\title{Optimizing vital nodes detection in complex distribution networks with a
global structural model}



\author[1]{Ling Yun Wang}%
\author[1]{Yang Li}%
\author[2]{Honglei Xu }%
\author[1]{ran Li}%
\author[1]{Yushan Zhou}%
\affil[1]{China Three Gorges University}%
\affil[2]{Curtin University School of Electrical Engineering Computing and Mathematical Sciences}%


\vspace{-1em}



  
  \date{April 25, 2026}


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\begin{abstract}
The identification of vulnerabilities within distribution networks,
impacted by complex factors such as ring configurations and the
integration of distributed energy resources, remains a significant
challenge. This study presents a novel enhanced hierarchical analysis
fault chain comprehensive assessment model to address this issue. The
proposed model develops four new weak indicators of assessing the nodes
of a distribution network based on complex network theory: an improved
indicator of node degree, improved indicator of node PageRank, power
flow increment impact indicator, and node voltage stability indicator.
It introduces the weighting method with the binomial coefficient for
optimal distribution of weight in the fault chain framework to further
enhance effectiveness in refined hierarchical analysis, aimed at
improving objectivity and precision. Besides, it employs an Integrated
Analytic Hierarchy Process in which system indicators and operational
parameters under various conditions can be integrated and dynamically
calculate the fault rate of a weak link in the network. These further
enhance the adaptability and precision of the model in solving inherent
complexities within modern distribution networks. Simulation case
studies performed using IEEE-69 node complex active distribution network
have demonstrated the efficacy and superiority of the proposed method
for correct identification of weak nodes.%
\end{abstract}%



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\textbf{Hosted file}

\verb`20250121.docx`  available at \url{https://authorea.com/users/594321/articles/1264882-optimizing-vital-nodes-detection-in-complex-distribution-networks-with-a-global-structural-model}

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