In this paper, we propose a novel methodology based on Koopman mode analysis to predict the transient stability of a power system in real-time. The method assesses the stability of the system based on a sliding sampling window of PMU measurements, and it detects the evolving instabilities by predicting future samples and investigating the computed Koopman eigenvalues. This approach is also able to identify alarm conditions, which include slowly evolving instabilities that may not be detected by predicting future samples in a limited time horizon. Identifying these conditions provides additional time to prepare a proper set of emergency control actions to be performed when necessary. Using the proposed method, groups of coherent generators that play roles in the evolving instabilities can also be identified, contributing to the design of a defensive islanding scheme for unstable cases. The efficacy of the proposed approach is demonstrated by simulating its performance with three test systems of different scales.

在本文中，我们提出了一种基于 Koopman 模式分析的新方法来实时预测电力系统的暂态稳定性。该方法基于 PMU 测量的滑动采样窗口评估系统的稳定性，并通过预测未来样本和研究计算的 Koopman 特征值来检测不断变化的不稳定性。这种方法还能够识别警报条件，其中包括通过在有限的时间范围内预测未来样本可能无法检测到的缓慢演变的不稳定性。识别这些条件可以提供额外的时间来准备一套适当的紧急控制行动，以便在必要时执行。使用所提出的方法，还可以识别在不断变化的不稳定性中起作用的相干发生器组，有助于为不稳定情况设计防御性孤岛方案。通过使用三个不同规模的测试系统模拟其性能，证明了所提出方法的有效性。