A Multi-Task Spatiotemporal Graph Neural Network for Transient Stability and State Prediction in Power Systems

Shuaibo Wang, Xinyuan Xiang, Jie Zhang, Zhuohang Liang, Shufang Li (), Peilin Zhong, Jie Zeng and Chenguang Wang
Additional contact information
Shuaibo Wang: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Xinyuan Xiang: 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
Shufang Li: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Peilin Zhong: 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
Chenguang Wang: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Energies, 2025, vol. 18, issue 6, 1-17

Abstract: Transient stability assessments and state prediction are critical tasks for power system security. The increasing integration of renewable energy sources has introduced significant uncertainties into these tasks. While AI has shown great potential, most existing AI-based approaches focus on single tasks, such as either stability assessments or state prediction, limiting their practical applicability. In power system operations, these two tasks are inherently coupled, as system states directly influence stability conditions. To address these challenges, this paper presents a multi-task learning framework based on spatiotemporal graph convolutional networks that efficiently performs both tasks. The proposed framework employs a spatiotemporal graph convolutional encoder to capture system topology features and integrates a self-attention U-shaped residual decoder to enhance prediction accuracy. Additionally, a Multi-Exit Network branch with confidence-based exit points enables efficient and reliable transient stability assessments. Experimental results on IEEE standard test systems and real-world power grids demonstrate the framework’s superiority as compared to state-of-the-art AI models, achieving a 48.1% reduction in prediction error, a 6.3% improvement in the classification F1 score, and a 52.1% decrease in inference time, offering a robust solution for modern power system monitoring and safety assessments.

Keywords: power system transient stability assessment; state prediction; multi-task 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: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/6/1531/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/6/1531/ (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:6:p:1531-:d:1616124

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 ().