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Research on vulnerability assessment method of electric power network based on graph neural
编号：72 访问权限：仅限参会人 更新：2023-11-20 13:45:39 浏览：512次 口头报告

报告开始：2023年12月09日 15:30（Asia/Shanghai）

报告时间：15min

所在会场：[S7] Power system protection and control [S7] Power system protection and control

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摘要
This paper proposes a vulnerability assessment method for power network based on graph neural network using complex network theory and graph neural network model. First, the eigenvector centrality (EC) in complex network theory is chosen as the measure of power system vulnerability. Second, an unsupervised power network vulnerability assessment model based on graph neural networks is established based on power system topological parameters and operational data. Finally, the learning effect and computational time efficiency of the model are explored, and the key nodes in the power network are identified.
 
关键词
Graph neural network; Complex network; Eigenvector Centrality;
报告人
Zijian Wan
student Southwest Jiaotong University

稿件作者
Zijian Wan Southwest Jiaotong University
Xu Liu Southwest Jiaotong University
Yeqing Zhang Southwest Jiaotong University
Yan Wang Southwest Jiaotong University
Yida Zeng Southwest Jiaotong University
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重要日期

  • 会议日期

    12月08日

    2023

    至

    12月10日

    2023

  • 11月01日 2023

    初稿截稿日期

  • 12月10日 2023

    注册截止日期

主办单位
IEEE IAS
承办单位
Southwest Jiaotong University (SWJTU)
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