面向变化电网拓扑的暂态稳定性：基于图嵌入动态特征的监督对比学习
Graph Embedding Dynamic Feature-based Supervised Contrastive Learning of Transient Stability for Changing Power Grid Topologies
AI总结：
针对电网拓扑变化下的在线暂态稳定预测，提出GEDF将拓扑结构编码为低维图嵌入流特征，并构建GEDF-SCL监督对比学习模型；IEEE 39节点系统的N-1与N-m-1事故仿真验证其高精度与拓扑适应性。
摘要：
准确的在线暂态稳定预测对于在遭受扰动时保障电力系统稳定至关重要。传统暂态稳定分析依赖时域仿真，无法快速适应电网拓扑变化。为将高维电网拓扑结构信息矢量化为基于节点的低维图嵌入流数据，本文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，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具有不同拓扑的电网。通过在这些生成的电力系统拓扑上仿真N-1和N-m-1事故，获取暂态运行数据。测试结果表明，GEDF-SCL模型能够在暂态稳定预测中实现高准确率，并能很好地适应不断变化的电网拓扑。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
