The real-time transient stability assessment (TSA) plays a critical role in the secure operation of the power system. Although the classic numerical integration method, \textit{i.e.} time-domain simulation (TDS), has been widely used in industry practice, it is inevitably trapped in a high computational complexity due to the high latitude sophistication of the power system. In this work, a data-driven power system estimation method is proposed to quickly predict the stability of the power system before TDS reaches the end of simulating time windows, which can reduce the average simulation time of stability assessment without loss of accuracy. As the topology of the power system is in the form of graph structure, graph neural network based representation learning is naturally suitable for learning the status of the power system. Motivated by observing the distribution information of crucial active power and reactive power on the power system's bus nodes, we thus propose a distribution-aware learning~(DAL) module to explore an informative graph representation vector for describing the status of a power system. Then, TSA is re-defined as a binary classification task, and the stability of the system is determined directly from the resulting graph representation without numerical integration. Finally, we apply our method to the online TSA task. The case studies on the IEEE 39-bus system and Polish 2383-bus system demonstrate the effectiveness of our proposed method.
翻译:实时瞬时稳定评估(TSA)在电力系统安全运行中发挥着关键作用。尽管经典数字集成法(\ textit{i.e.}时间-域模拟(TDS)在行业实践中被广泛使用,但由于电源系统的高纬度精密性,它不可避免地被困在高度的计算复杂性中。在这项工作中,提出了数据驱动的动力系统评估方法,以便在电源系统模拟时间窗口到达终端之前迅速预测电力系统的稳定性,这可以减少稳定评估的平均模拟时间,而不会丧失准确性。由于电源系统的表层结构是图表结构,基于图形神经网络的模拟学习自然适合于学习学习动力系统的状况。通过观察电源系统总节点上关键主动动力和反应力的分布信息,我们因此提议了一个分布觉学习~(DAL)模块,以探索描述电源系统状况的信息性图表显示矢量的矢量矢量矢量矢量矢量矢量矢量矢量矢量。然后,TSA被重新定义为二元分类任务,基于图形网络神经网络代表学习自然适合了解电源系统的状况。通过TE83号任务方式,我们的数据代表系统最终从图表演示演示了23分析系统。