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

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.