An Advanced Spatio-Temporal Graph Neural Network Framework for the Concurrent Prediction of Transient and Voltage Stability

Chaoping Deng, Liyu Dai, Wujie Chao, Junwei Huang, Jinke Wang, Lanxin Lin, Wenyu Qin, Shengquan Lai and Xin Chen ()
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Chaoping Deng: State Grid Fujian Electric Power Research Institute, Fuzhou 350007, China
Liyu Dai: State Grid Fujian Electric Power Research Institute, Fuzhou 350007, China
Wujie Chao: State Grid Fujian Electric Power Research Institute, Fuzhou 350007, China
Junwei Huang: State Grid Fujian Electric Power Research Institute, Fuzhou 350007, China
Jinke Wang: State Grid Fujian Electric Power Research Institute, Fuzhou 350007, China
Lanxin Lin: State Grid Fujian Electric Power Co., Ltd. Fuzhou Company, Fuzhou 350007, China
Wenyu Qin: The School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Shengquan Lai: The School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Xin Chen: The School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Energies, 2025, vol. 18, issue 3, 1-17

Abstract: Power system stability prediction leveraging deep learning has gained significant attention due to the extensive deployment of phasor measurement units. However, most existing methods focus on predicting either transient or voltage stability independently. In real-world scenarios, these two types of instability often co-occur, necessitating distinct and coordinated control strategies. This paper presents a novel concurrent prediction framework for transient and voltage stability using a spatio-temporal embedding graph neural network (STEGNN). The proposed framework utilizes a graph neural network to extract topological features of the power system from adjacency matrices and temporal data graphs. In contrast, a temporal convolutional network captures the system’s dynamic behavior over time. A weighted loss function is introduced during training to enhance the model’s ability to handle instability cases. Experimental validation on the IEEE 118-bus system demonstrates the superiority of the proposed method compared to single stability prediction approaches. The STEGNN model is further evaluated for its prediction efficiency and robustness to measurement noise. Moreover, results highlight the model’s strong transfer learning capability, successfully transferring knowledge from an N-1 contingency dataset to an N-2 contingency dataset.

Keywords: transient stability; voltage stability; graph neural network; temporal convolutional network; concurrent prediction (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
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