Controlled Power System Separation Using Generator PMU Data and System Kinetic Energy
Ilya Tyuryukanov, Marjan Popov, Jorrit A. Bos, Mart A. M. M. van der Meijden, Vladimir Terzija
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With the growing number of severe system disturbances and blackouts around the world, controlled system separation is becoming an increasingly important system integrity protection scheme (SIPS) to save the electric power system from a complete or partial disintegration. A successful controlled splitting approach should at least tackle the following two well-known and interrelated problems: "when to split?" and "where to split?". Multiple previous publications consider these problems separately, and even those pursuing a combined approach propose solutions with limited applicability. In this paper, we are proposing a novel PMU-based detector of loss of synchronism that utilizes generator rotor angles and speeds to promptly detect rotor angle instabilities over a wide area. Moreover, we are showing how our loss of synchronism detection principle can be coupled with the known controlled splitting techniques to form an integrated defense scheme against unintentional loss of synchronism. The performance of this wide-area SIPS is demonstrated on the IEEE 39-bus test power system for various types of unstable conditions.
PMU-based dynamic state and parameter estimation for dynamic security assessment in power systems -- Ultimate boundedness in the presence of measurement noise
pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events
介绍了第一个数据驱动的电力系统事件数据生成模型pmuGE（phasor measurement unit Generator of Events）。我们已经在数千个实际事件上训练了这个模型，并创建了一个名为pmuBAGE（生成的PMU事件的基准分类）的数据集。该数据集由近1000个标记事件数据实例组成，以鼓励对相量测量单元（PMU）数据分析进行基准评估。PMU数据很难获得，特别是那些涵盖事件周期的数据。然而，电力系统问题最近通过数据驱动的机器学习解决方案取得了惊人的进步。一个高度可访问的标准基准数据集将大大加速该领域成功的机器学习技术的发展。提出了一种基于电力系统事件参与分解的PMU数据学习方法，使系统异常时PMU数据生成模型的学习成为可能。该模型可以创建高度真实的事件数据，而不会损害用于训练它的PMU的差异隐私。该数据集可供任何研究人员或从业者在线使用，请访问pmuBAGE Github存储库：https://github.com/NanpengYu/pmuBAGE。
