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.

翻译：准确的在线暂态稳定预测对于确保电力系统在遭遇扰动时的稳定性至关重要。传统的暂态稳定分析依赖于时域仿真，难以快速适应电网拓扑的变化。为将高维电网拓扑结构信息向量化为基于节点的低维图嵌入流数据，提出了图嵌入动态特征（GEDF）。基于GEDF的暂态稳定监督对比学习（GEDF-SCL）模型利用监督对比学习并结合电网拓扑信息，通过GEDF预测暂态稳定性。为评估所提GEDF-SCL模型的性能，基于IEEE 39节点系统模型生成了不同拓扑的电网。通过对这些生成的电力系统拓扑模拟N-1和N-$\bm{m}$-1故障，获取了暂态运行数据。测试结果表明，GEDF-SCL模型能够实现高精度的暂态稳定预测，并能很好地适应变化的电网拓扑。