Synthetic Dynamic PMU Data Generation: A Generative Adversarial Network Approach
It addresses data scarcity for power systems research and education, but is incremental as it applies an existing method to a new domain.
The paper tackles the lack of accessible real phasor measurement unit (PMU) data by proposing a generative adversarial network (GAN) to directly generate synthetic PMU data, validating it through theoretical analysis and quantitative evaluation, though noting room for improvement.
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.