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

TL;DR

This paper presents pmuBAGE, a new benchmark dataset of generated power system event data created by a novel generative model trained on real events, facilitating machine learning research in power system anomaly detection.

Contribution

The paper introduces pmuBAGE, a large labeled dataset of generated PMU event data and a novel learning method based on Event Participation Decomposition for realistic data synthesis.

Findings

Generated data closely mimics real power system events

The dataset supports benchmarking of PMU data analytics methods

The generative model preserves differential privacy of training data

Abstract

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.