Transient Stability Analysis and Emergency Generator Tripping Control Based on Spatio-Temporal Graph Deep Learning

Shuaibo Wang, Jie Zeng, Jie Zhang, Zhuohang Liang, Yihua Zhu and Shufang Li ()
Additional contact information
Shuaibo Wang: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Jie Zeng: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Jie Zhang: State Key Laboratory of HVDC, Electric Power Research Institute, China Southern Power Grid, Guangzhou 510663, China
Zhuohang Liang: Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System, Guangzhou 510663, China
Yihua Zhu: National Energy Power Grid Technology R&D Centre, Guangzhou 510663, China
Shufang Li: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Energies, 2025, vol. 18, issue 4, 1-24

Abstract: This paper addresses the challenge of achieving fast and accurate transient stability analysis and emergency control in power systems, which are crucial for reliable grid operation under disturbances. To this end, we propose a spatio-temporal graph deep learning approach leveraging Diffusion Convolutional Gated Recurrent Units (DCGRUs) for transient stability assessment and coherent generator group prediction. Unlike traditional methods, our approach explicitly represents transient responses as spatio-temporal graph data, capturing both topological and dynamic dependencies. The DCGRU model effectively extracts these features, and the predicted coherent generator groups are incorporated into the single-machine infinite-bus equivalence method to design an emergency generator tripping scheme. Simulation analysis results on both benchmark and real-world power grids validate the proposed method’s feasibility and effectiveness in enhancing transient stability analysis and emergency control.

Keywords: emergency control; power system stability; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/4/993/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/4/993/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:4:p:993-:d:1594192

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().