3 / 2023-07-17 17:23:17
Transient stability assessment of power systems with graph neural networks considering global features
Graph Neural Networks; Transient stability assessment; Global Features; Multi-dimensional features.
Draft Accepted
Shengyuan Yang / Southwest Jiaotong University
Mengxiang Ding / Southwest Jiaotong University
Zijian Wan / Southwest Jiaotong University
Haichuan Yang / Southwest Jiaotong University
Yilin Liu / Southwest jiaotong university
Wenli Fan / Southwest Jiaotong University
Currently, the transient stability assessment of power systems using graph neural networks often overlooks the multidimensional characteristics of transmission lines and exhibits limited utilization of overarching features. To address this issue, this paper introduces a novel framework for graph neural networks, termed Global Features-Exploiting Edge Features for Graph Convolutional Networks (G-EGCN), specifically designed for transient stability assessment in power systems while considering global features. The proposed framework effectively harnesses the complete graph information of the power system by aggregating node features, edge features, and global features. Ultimately, a comprehensive validation of the proposed model's performance is conducted through simulation and comparative analysis on a 10-machine 39-node system.
Important Date
  • Conference Date

    Dec 08

    2023

    to

    Dec 10

    2023

  • Nov 01 2023

    Draft paper submission deadline

  • Dec 10 2023

    Registration deadline

Sponsored By
IEEE IAS
Organized By
Southwest Jiaotong University (SWJTU)