Creation of Synthetic Networked PMU Data: A Generative Adversarial Network Approach

TL;DR

This paper presents a novel GAN-based method for generating realistic synthetic PMU data streams that capture dynamic characteristics and can enhance event classification performance in power systems.

Contribution

It introduces a GAN approach for directly learning from real PMU data to produce multiple realistic synthetic data streams, improving upon traditional simulation methods.

Findings

Synthetic data closely resembles real PMU data statistically.

The generated data captures meaningful dynamic characteristics.

Using synthetic data improves event classification accuracy.

Abstract

This paper introduces a machine learning-based approach to synthetically creating realistic phasor measurement unit (PMU) data streams of multiple transient types. In contrast to the existing literature of transient simulation-based data generation methods, we propose a generative adversarial network (GAN) based approach to learning directly from the historical data and simultaneously reproduce multiple PMU data streams. The synthetic PMU data streams reflect meaningful dynamic characteristics which observe first principles such as Kirchhoff's laws. The efficacy of this approach is demonstrated by numerical studies on the IEEE 39-bus system. We validate the fidelity and flexibility of the synthetic data via statistical resemblance and modal analysis approaches. Finally we illustrate a practical application scenario for the usage of the synthetic PMU data, i.e. leverage the synthetic data to improve the performance of the event classification algorithms.