  Post-fault dynamics of short-term voltage stability (SVS) present
spatial-temporal characteristics, but the existing data-driven methods for
online SVS assessment fail to incorporate such characteristics into their
models effectively. Confronted with this dilemma, this paper develops a novel
spatial-temporal graph convolutional network (STGCN) to address this problem.
The proposed STGCN utilizes graph convolution to integrate network topology
information into the learning model to exploit spatial information. Then, it
adopts one-dimensional convolution to exploit temporal information. In this
way, it models the spatial-temporal characteristics of SVS with complete
convolutional structures. After that, a node layer and a system layer are
strategically designed in the STGCN for SVS assessment. The proposed STGCN
incorporates the characteristics of SVS into the data-driven classification
model. It can result in higher assessment accuracy, better robustness and
adaptability than conventional methods. Besides, parameters in the system layer
can provide valuable information about the influences of individual buses on
SVS. Test results on the real-world Guangdong Power Grid in South China verify
the effectiveness of the proposed network.
