This paper introduces 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. 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. 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 or practitioner to use at the pmuBAGE Github Repository: https://github.com/NanpengYu/pmuBAGE.
翻译:本文介绍PmuGE(光量测量单位事件生成器),这是第一个由数据驱动的动力系统事件数据生成模型。我们已经就数千个实际事件对这个模型进行了培训,并创建了一个称为PmuBAGE(生成的PMU事件的基准排序)的数据集。数据集包括近1000个标签事件数据实例,以鼓励对声波测量单位(PMU)数据分析进行基准评估。PMU数据很难获得,特别是涵盖事件期间的数据。然而,最近,电动系统问题通过数据驱动机器学习解决方案出现了惊人的进展。一个高度易得手的标准基准数据集将大大加速开发这一领域的成功机器学习技术。我们提出了一个基于电源系统事件参与配置的新式学习方法,以便能够在系统异常期间学习一个PMU数据的基因化模型。该模型可以创建高度现实的事件数据,而不会损害用于培训它的PMU的隐私差异性。该数据集可供任何研究人员或开业者在线使用,用于在 pmuBAGI/MUBUBAGYAGUMATIM. MAGAGAGYGUBRpostoryary: MAGAGAGAGYGYGAGUBATIMOOYGYGYGYGISOMOMOMOYGISMOMOYAYAYAYAYAYGYGYGYGYGYGYATIMATIMAYATIMAMAYAMAMAMAMAYMAYMAYMAYMAYMAYMAYMAYMAYMAYMAYMATIMATIMATIMAYGIS)