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Accurate online transient stability prediction is critical for ensuring powersystem stability when facing disturbances. While traditional transient stablityanalysis replies on the time domain simulations can not be quickly adapted tothe power grid toplogy change. In order to vectorize high-dimensional powergrid topological structure information into low-dimensional node-based graphembedding streaming data, graph embedding dynamic feature (GEDF) has beenproposed. The transient stability GEDF-based supervised contrastive learning(GEDF-SCL) model uses supervised contrastive learning to predict transientstability with GEDFs, considering power grid topology information. To evaluatethe performance of the proposed GEDF-SCL model, power grids of varyingtopologies were generated based on the IEEE 39-bus system model. Transientoperational data was obtained by simulating N-1 and N-$\bm{m}$-1 contingencieson these generated power system topologies. Test result demonstrated that theGEDF-SCL model can achieve high accuracy in transient stability prediction andadapt well to changing power grid topologies.

Graph Embedding Dynamic Feature-based Supervised Contrastive Learning of Transient Stability for Changing Power Grid Topologies