  Facing the difficulty of expensive and trivial data collection and
annotation, how to make a deep learning-based short-term voltage stability
assessment (STVSA) model work well on a small training dataset is a challenging
and urgent problem. Although a big enough dataset can be directly generated by
contingency simulation, this data generation process is usually cumbersome and
inefficient; while data augmentation provides a low-cost and efficient way to
artificially inflate the representative and diversified training datasets with
label preserving transformations. In this respect, this paper proposes a novel
deep-learning intelligent system incorporating data augmentation for STVSA of
power systems. First, due to the unavailability of reliable quantitative
criteria to judge the stability status for a specific power system,
semi-supervised cluster learning is leveraged to obtain labeled samples in an
original small dataset. Second, to make deep learning applicable to the small
dataset, conditional least squares generative adversarial networks
(LSGAN)-based data augmentation is introduced to expand the original dataset
via artificially creating additional valid samples. Third, to extract temporal
dependencies from the post-disturbance dynamic trajectories of a system, a
bi-directional gated recurrent unit with attention mechanism based assessment
model is established, which bi-directionally learns the significant time
dependencies and automatically allocates attention weights. The test results
demonstrate the presented approach manages to achieve better accuracy and a
faster response time with original small datasets. Besides classification
accuracy, this work employs statistical measures to comprehensively examine the
performance of the proposal.
