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This paper introduces a machine learning-based approach to syntheticallycreating realistic phasor measurement unit (PMU) data streams of multipletransient types. In contrast to the existing literature of transientsimulation-based data generation methods, we propose a generative adversarialnetwork (GAN) based approach to learning directly from the historical data andsimultaneously reproduce multiple PMU data streams. The synthetic PMU datastreams reflect meaningful dynamic characteristics which observe firstprinciples such as Kirchhoff's laws. The efficacy of this approach isdemonstrated by numerical studies on the IEEE 39-bus system. We validate thefidelity and flexibility of the synthetic data via statistical resemblance andmodal analysis approaches. Finally we illustrate a practical applicationscenario for the usage of the synthetic PMU data, i.e. leverage the syntheticdata to improve the performance of the event classification algorithms.

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