  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.
