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

arXiv: https://arxiv.org/abs/2210.14204 · HTML: https://mlsanity.com/paper/2210.14204/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 46 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [The Creation and Validation of Load Time Series for Synthetic Electric Power Systems](https://mlsanity.com/paper/1911.06934/) (2019)
- [Power System Event Identification based on Deep Neural Network with Information Loading](https://mlsanity.com/paper/2011.06718/) (2020)
- [Real-time Anomaly Detection and Classification in Streaming PMU Data](https://mlsanity.com/paper/1911.06316/) (2019)
- [Synthetic Dynamic PMU Data Generation: A Generative Adversarial Network Approach](https://mlsanity.com/paper/1812.03203/) (2018)
- [Creation of Synthetic Networked PMU Data: A Generative Adversarial Network Approach](https://mlsanity.com/paper/1908.08180/) (2019)

## Abstract (arXiv)

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.