Transient Stability Assessment of Power Systems Built upon Attention-Based Spatial–Temporal Graph Convolutional Networks

Yu Nan, Weiping Niu, Yong Chang, Zhenzhen Kong and Huichao Zhao ()
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Yu Nan: State Grid Henan Electric Power Company Kaifeng Power Supply Company, Kaifeng 475000, China
Weiping Niu: State Grid Henan Electric Power Company Kaifeng Power Supply Company, Kaifeng 475000, China
Yong Chang: State Grid Henan Electric Power Company Kaifeng Power Supply Company, Kaifeng 475000, China
Zhenzhen Kong: State Grid Henan Electric Power Company Kaifeng Power Supply Company, Kaifeng 475000, China
Huichao Zhao: Department of Electrical Power Engineering, North China Electric Power University, Baoding 071000, China

Energies, 2025, vol. 18, issue 14, 1-18

Abstract: Rapid and accurate transient stability assessment (TSA) is crucial for ensuring secure and stable operation in power systems. However, existing methods fail to adequately exploit the spatiotemporal characteristics in power grid transient data, which constrains the evaluation performance of models. This paper proposes a TSA method built upon an Attention-Based Spatial–Temporal Graph Convolutional Network (ASTGCN) model. First, a spatiotemporal attention module is used to aggregate and extract the spatiotemporal correlations of the transient process in the power system. A spatiotemporal convolution module is then employed to effectively capture the spatial features and temporal evolution patterns of transient stability data. In addition, an adaptive focal loss function is designed to enhance the fitting of unstable samples and increase the weight of misclassified samples, thereby improving global accuracy and reducing the occurrence of missed instability samples. Finally, the simulation results from the New England 10-machine 39-bus system and the NPCC 48-machine 140-bus system validate the effectiveness of the proposed methodology.

Keywords: power systems; transient stability assessment; spatiotemporal characteristics; spatiotemporal convolution module (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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