论文标题
PMUBAGE：用于电源系统事件的PMU数据的基准分类 - 第一部分：概述和结果
pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events -- Part I: Overview and Results
论文作者
论文摘要
我们提出了PMUGE（相组测量单元的事件生成器），这是电力系统事件数据的第一个数据驱动的生成模型之一。我们已经对数千个实际事件进行了培训，并创建了一个表示为PMUBAGE的数据集（生成的PMU事件的基准分类）。该数据集由几乎1000个标记的事件数据实例组成，以鼓励对相量测量单元（PMU）数据分析进行基准评估。该数据集可在线使用，可供该领域的任何研究人员或从业者使用。 PMU数据挑战，尤其是那些涵盖事件期间的数据。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看到了惊人的进步 - 这是由很幸运能够获得此类PMU数据的研究人员创建的解决方案。高度可访问的标准基准测定数据集将使该领域成功的机器学习技术的开发急剧加速。我们提出了一种基于功率系统事件的事件参与分解的新型学习方法，这使得在系统异常过程中可以学习PMU数据的生成模型。该模型可以创建高度现实的事件数据，而不会损害用于训练它的PMU的差异隐私。该数据集可在线使用，供任何研究人员在PMUBAGE GITHUB存储库中使用-https：//github.com/nanpengyu/pmubage。 第一部分 - 这是两部分论文的第一部分。在第一部分中，我们描述了PMUBAGE，其创建的高级概述以及用于测试它的实验。第二部分将更详细地讨论其一代中使用的精确模型。
We present pmuGE (phasor measurement unit Generator of Events), one of the first data-driven generative model for power system event data. We have trained this model on thousands of actual events and created a dataset denoted pmuBAGE (the Benchmarking Assortment of Generated PMU Events). The dataset consists of almost 1000 instances of labeled event data to encourage benchmark evaluations on phasor measurement unit (PMU) data analytics. The dataset is available online for use by any researcher or practitioner in the field. PMU data are challenging to obtain, especially those covering event periods. Nevertheless, power system problems have recently seen phenomenal advancements via data-driven machine learning solutions - solutions created by researchers who were fortunate enough to obtain such PMU data. A highly accessible standard benchmarking dataset would enable a drastic acceleration of the development of successful machine learning techniques in this field. We propose a novel learning method based on the Event Participation Decomposition of Power System Events, which makes it possible to learn a generative model of PMU data during system anomalies. The model can create highly realistic event data without compromising the differential privacy of the PMUs used to train it. The dataset is available online for any researcher to use at the pmuBAGE Github Repository - https://github.com/NanpengYu/pmuBAGE. Part I - This is part I of a two part paper. In part I, we describe a high level overview of pmuBAGE, its creation, and the experiments used to test it. Part II will discuss the exact models used in its generation in far more detail.
