[Oral Presentation]Transient stability assessment of power systems with graph neural networks considering global features

Transient stability assessment of power systems with graph neural networks considering global features
ID:4 Submission ID:3 View Protection:ATTENDEE Updated Time:2023-11-20 13:45:30 Hits:413 Oral Presentation

Start Time:2023-12-10 09:15 (Asia/Shanghai)

Duration:15min

Session:[S8] AI-driven technology » [S8] AI-driven technology

Presentation File

Tips: The file permissions under this presentation are only for participants. You have not logged in yet and cannot view it temporarily.

Abstract
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.
Keywords
Graph Neural Networks; Transient stability assessment; Global Features; Multi-dimensional features.
Speaker
Shengyuan Yang
student Southwest Jiaotong University

Submission Author
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
Comment submit
Verification code Change another
All comments

Contact us

Southwest Jiaotong University

(SWJTU)

Add: No.999, Xi'an Road, Pidu

District, Chengdu City, Sichuan

Province,611756 China

Email: ciycee2023@163.com

 

Aconf Staff:Lu Wei

Tel:+86 18971567453
Email:luwei@chytey.com

WeChat public account: 

IEEE IAS SWJTU Student Branch