  To fully learn the latent temporal dependencies from post-disturbance system
dynamic trajectories, deep learning is utilized for short-term voltage
stability (STVS) assessment of power systems in this paper. First of all, a
semi-supervised cluster algorithm is performed to obtain class labels of STVS
instances due to the unavailability of reliable quantitative criteria.
Secondly, a long short-term memory (LSTM) based assessment model is built
through learning the time dependencies from the post-disturbance system
dynamics. Finally, the trained assessment model is employed to determine the
systems stability status in real time. The test results on the IEEE 39-bus
system suggest that the proposed approach manages to assess the stability
status of the system accurately and timely. Furthermore, the superiority of the
proposed method over traditional shallow learning-based assessment methods has
also been proved.
