Synthetic Dynamic PMU Data Generation: A Generative Adversarial Network Approach

TL;DR

This paper introduces a novel GAN-based method for generating realistic synthetic PMU data, addressing confidentiality issues and aiding research and education without needing detailed power system models.

Contribution

It is the first application of a generative adversarial network to synthesize PMU data directly from real measurements, bypassing complex system modeling.

Findings

GAN can effectively learn system dynamics from real PMU data

Synthetic data closely mimics real PMU measurements

The approach has room for further improvement

Abstract

This paper concerns with the production of synthetic phasor measurement unit (PMU) data for research and education purposes. Due to the confidentiality of real PMU data and no public access to the real power systems infrastructure information, the lack of credible realistic data becomes a growing concern. Instead of constructing synthetic power grids and then producing synthetic PMU measurement data by time simulations, we propose a model-free approach to directly generate synthetic PMU data. we train the generative adversarial network (GAN) with real PMU data, which can be used to generate synthetic PMU data capturing the system dynamic behaviors. To validate the sequential generation by GAN to mimic PMU data, we theoretically analyze GAN's capacity of learning system dynamics. Further by evaluating the synthetic PMU data by a proposed quantitative method, we verify GAN's potential to synthesize realistic samples and meanwhile realize that GAN model in this paper still has room to improve. Moreover it is the first time that such generative model is applied to synthesize PMU data.