A Multi-Task Spatiotemporal Graph Neural Network for Transient Stability and State Prediction in Power Systems
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 t...
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| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/6/1531 |
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| author | Shuaibo Wang Xinyuan Xiang Jie Zhang Zhuohang Liang Shufang Li Peilin Zhong Jie Zeng Chenguang Wang |
| author_facet | Shuaibo Wang Xinyuan Xiang Jie Zhang Zhuohang Liang Shufang Li Peilin Zhong Jie Zeng Chenguang Wang |
| author_sort | Shuaibo Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0d74a01253be42cdb6860c33399c85db |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-0d74a01253be42cdb6860c33399c85db2025-08-20T02:11:19ZengMDPI AGEnergies1996-10732025-03-01186153110.3390/en18061531A Multi-Task Spatiotemporal Graph Neural Network for Transient Stability and State Prediction in Power SystemsShuaibo Wang0Xinyuan Xiang1Jie Zhang2Zhuohang Liang3Shufang Li4Peilin Zhong5Jie Zeng6Chenguang Wang7School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Key Laboratory of HVDC, Electric Power Research Institute, China Southern Power Grid, Guangzhou 510663, ChinaGuangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System, Guangzhou 510663, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaTransient 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.https://www.mdpi.com/1996-1073/18/6/1531power system transient stability assessmentstate predictionmulti-task learning |
| spellingShingle | Shuaibo Wang Xinyuan Xiang Jie Zhang Zhuohang Liang Shufang Li Peilin Zhong Jie Zeng Chenguang Wang A Multi-Task Spatiotemporal Graph Neural Network for Transient Stability and State Prediction in Power Systems Energies power system transient stability assessment state prediction multi-task learning |
| title | A Multi-Task Spatiotemporal Graph Neural Network for Transient Stability and State Prediction in Power Systems |
| title_full | A Multi-Task Spatiotemporal Graph Neural Network for Transient Stability and State Prediction in Power Systems |
| title_fullStr | A Multi-Task Spatiotemporal Graph Neural Network for Transient Stability and State Prediction in Power Systems |
| title_full_unstemmed | A Multi-Task Spatiotemporal Graph Neural Network for Transient Stability and State Prediction in Power Systems |
| title_short | A Multi-Task Spatiotemporal Graph Neural Network for Transient Stability and State Prediction in Power Systems |
| title_sort | multi task spatiotemporal graph neural network for transient stability and state prediction in power systems |
| topic | power system transient stability assessment state prediction multi-task learning |
| url | https://www.mdpi.com/1996-1073/18/6/1531 |
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