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Self-Organized Criticality Identification of Power System Based on SC-GCN Network
编号：97 访问权限：仅限参会人 更新：2023-11-20 13:45:42 浏览：517次 口头报告

报告开始：2023年12月10日 11:00（Asia/Shanghai）

报告时间：15min

所在会场：[S8] AI-driven technology [S8] AI-driven technology

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摘要
In order to solve the problem that the traditional power system self-organized criticality identification method is weak in dealing with graphical data and nonlinear coupling factors, this paper proposes a power system self-organized criticality identification method based on SC-GCN neural network. First, the self-organized criticality evolution process is simulated based on the OPA model. Secondly, the feature quantities of each day, including bus features, line features and global features, are obtained according to the self-organized criticality evolution process, and the data are processed and classified as graph data. Further, a SC-GCN neural network model is obtained by adding jump connections to the traditional graph convolutional neural network to solve the gradient vanishing problem and accelerate the model convergence. Finally, the dataset is input into the SC-GCN neural network model for training and testing, and the simulation results show that compared with the traditional power system self-organized criticality identification method, the power system self-organized criticality identification method proposed in this paper can be more perfect, simple and rapid to identify the power system self-organized criticality online.
关键词
Graph Convolutional Neural Networks; Self-Organizing Criticality; OPA Model; Jump Connections;
报告人
Liyang Liu
researcher State Grid Sichuan Economic Research Institute

稿件作者
Liyang Liu State Grid Sichuan Economic Research Institute
Yuqi Han State Grid Sichuan Economic Research Institute;
Shengyong Ye State Grid Sichuan Economic Research Institute
Chuan Long State Grid Sichuan Economic Research Institute;
Zixuan Liu Southwest Jiaotong University
Xuna Liu State Grid Sichuan Economic Research Institute;
Ting Li State Grid Sichuan Economic Research Institute;
Xinting Yang State Grid Sichuan Economic Research Institute;
Da Li State Grid Sichuan Economic Research Institute;
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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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