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Post-fault dynamics of short-term voltage stability (SVS) presentspatial-temporal characteristics, but the existing data-driven methods foronline SVS assessment fail to incorporate such characteristics into theirmodels effectively. Confronted with this dilemma, this paper develops a novelspatial-temporal graph convolutional network (STGCN) to address this problem.The proposed STGCN utilizes graph convolution to integrate network topologyinformation into the learning model to exploit spatial information. Then, itadopts one-dimensional convolution to exploit temporal information. In thisway, it models the spatial-temporal characteristics of SVS with completeconvolutional structures. After that, a node layer and a system layer arestrategically designed in the STGCN for SVS assessment. The proposed STGCNincorporates the characteristics of SVS into the data-driven classificationmodel. It can result in higher assessment accuracy, better robustness andadaptability than conventional methods. Besides, parameters in the system layercan provide valuable information about the influences of individual buses onSVS. Test results on the real-world Guangdong Power Grid in South China verifythe effectiveness of the proposed network.

Data-Driven Short-Term Voltage Stability Assessment Based on Spatial-Temporal Graph Convolutional Network