  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.
