Graph Embedding Dynamic Feature-based Supervised Contrastive Learning of
  Transient Stability for Changing Power Grid Topologies
Zijian Lv Xin Chen Zijian Feng
Accurate online transient stability prediction is critical for ensuring power
system stability when facing disturbances. While traditional transient stablity
analysis replies on the time domain simulations can not be quickly adapted to
the power grid toplogy change. In order to vectorize high-dimensional power
grid topological structure information into low-dimensional node-based graph
embedding streaming data, graph embedding dynamic feature (GEDF) has been
proposed. The transient stability GEDF-based supervised contrastive learning
(GEDF-SCL) model uses supervised contrastive learning to predict transient
stability with GEDFs, considering power grid topology information. To evaluate
the performance of the proposed GEDF-SCL model, power grids of varying
topologies were generated based on the IEEE 39-bus system model. Transient
operational data was obtained by simulating N-1 and N-$\bm{m}$-1 contingencies
on these generated power system topologies. Test result demonstrated that the
GEDF-SCL model can achieve high accuracy in transient stability prediction and
adapt well to changing power grid topologies.