  Transient stability prediction is critically essential to the fast online
assessment and maintaining the stable operation in power systems. The wide
deployment of phasor measurement units (PMUs) promotes the development of
data-driven approaches for transient stability assessment. This paper proposes
the temporal and topological embedding deep neural network (TTEDNN) model to
forecast transient stability with the early transient dynamics. The TTEDNN
model can accurately and efficiently predict the transient stability by
extracting the temporal and topological features from the time-series data of
the early transient dynamics. The grid-informed adjacency matrix is used to
incorporate the power grid structural and electrical parameter information. The
transient dynamics simulation environments under the single-node and
multiple-node perturbations are used to test the performance of the TTEDNN
model for the IEEE 39-bus and IEEE 118-bus power systems. The results show that
the TTEDNN model has the best and most robust prediction performance.
Furthermore, the TTEDNN model also demonstrates the transfer capability to
predict the transient stability in the more complicated transient dynamics
simulation environments.
