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Transient stability prediction is critically essential to the fast onlineassessment and maintaining the stable operation in power systems. The widedeployment of phasor measurement units (PMUs) promotes the development ofdata-driven approaches for transient stability assessment. This paper proposesthe temporal and topological embedding deep neural network (TTEDNN) model toforecast transient stability with the early transient dynamics. The TTEDNNmodel can accurately and efficiently predict the transient stability byextracting the temporal and topological features from the time-series data ofthe early transient dynamics. The grid-informed adjacency matrix is used toincorporate the power grid structural and electrical parameter information. Thetransient dynamics simulation environments under the single-node andmultiple-node perturbations are used to test the performance of the TTEDNNmodel for the IEEE 39-bus and IEEE 118-bus power systems. The results show thatthe TTEDNN model has the best and most robust prediction performance.Furthermore, the TTEDNN model also demonstrates the transfer capability topredict the transient stability in the more complicated transient dynamicssimulation environments.

Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network