PmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events

Abstract

This paper introduces two contributions - a generative architecture called pmuGE (phasor measurement unit Generator of Events), and a dataset generated by pmuGE called pmuBAGE (the Benchmarking Assortment of Generated PMU Events). The architecture, pmuGE, is one of the first data-driven generative models for power system event data. The dataset, pmuBAGE, is a high quality dataset trained on thousands of actual events using the pmuGE architecture. 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 <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NanpengYu/pmuBAGE</uri> Github Repository.