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The real-time transient stability assessment (TSA) plays a critical role inthe secure operation of the power system. Although the classic numericalintegration method, \textit{i.e.} time-domain simulation (TDS), has been widelyused in industry practice, it is inevitably trapped in a high computationalcomplexity due to the high latitude sophistication of the power system. In thiswork, a data-driven power system estimation method is proposed to quicklypredict the stability of the power system before TDS reaches the end ofsimulating time windows, which can reduce the average simulation time ofstability assessment without loss of accuracy. As the topology of the powersystem is in the form of graph structure, graph neural network basedrepresentation learning is naturally suitable for learning the status of thepower system. Motivated by observing the distribution information of crucialactive power and reactive power on the power system's bus nodes, we thuspropose a distribution-aware learning~(DAL) module to explore an informativegraph representation vector for describing the status of a power system. Then,TSA is re-defined as a binary classification task, and the stability of thesystem is determined directly from the resulting graph representation withoutnumerical integration. Finally, we apply our method to the online TSA task. Thecase studies on the IEEE 39-bus system and Polish 2383-bus system demonstratethe effectiveness of our proposed method.

Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System