  Deep learning (DL) algorithms have been widely applied to short-term voltage
stability (STVS) assessment in power systems. However, transferring the
knowledge learned in one power grid to other power grids with topology changes
is still a challenging task. This paper proposed a transferable DL-based model
for STVS assessment by constructing the topology-aware voltage dynamic features
from raw PMU data. Since the reactive power flow and grid topology are
essential to voltage stability, the topology-aware and physics-informed voltage
dynamic features are utilized to effectively represent the topological and
temporal patterns from post-disturbance system dynamic trajectories. The
proposed DL-based STVS assessment model is tested under random operating
conditions on the New England 39-bus system. It has 99.99\% classification
accuracy of the short-term voltage stability status using the topology-aware
and physics-informed voltage dynamic features. In addition to high accuracy,
the experiments show good adaptability to PMU errors. Moreover, The proposed
STVS assessment method has outstanding performance on new grid topologies after
fine-tuning. In particular, the highest accuracy reaches 99.68\% in evaluation,
which demonstrates a good knowledge transfer ability of the proposed model for
power grid topology change.
