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We present pmuGE (phasor measurement unit Generator of Events), one of thefirst data-driven generative model for power system event data. We have trainedthis model on thousands of actual events and created a dataset denoted pmuBAGE(the Benchmarking Assortment of Generated PMU Events). The dataset consists ofalmost 1000 instances of labeled event data to encourage benchmark evaluationson phasor measurement unit (PMU) data analytics. The dataset is availableonline for use by any researcher or practitioner in the field. PMU data arechallenging to obtain, especially those covering event periods. Nevertheless,power system problems have recently seen phenomenal advancements viadata-driven machine learning solutions - solutions created by researchers whowere fortunate enough to obtain such PMU data. A highly accessible standardbenchmarking dataset would enable a drastic acceleration of the development ofsuccessful machine learning techniques in this field. We propose a novellearning method based on the Event Participation Decomposition of Power SystemEvents, which makes it possible to learn a generative model of PMU data duringsystem anomalies. The model can create highly realistic event data withoutcompromising the differential privacy of the PMUs used to train it. The datasetis 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 highlevel 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 moredetail.

pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events -- Part I: Overview and Results