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The availability of large datasets is crucial for the development of new power system applications and tools; unfortunately, very few are publicly and freely available. The authors designed an end‐to‐end generative framework for the creation of synthetic bus‐level time‐series load data for transmission networks. The model is trained on a real dataset of over 70 Terabytes of synchrophasor measurements spanning multiple years. Leveraging a combination of principal component analysis and conditional generative adversarial network models, the developed scheme allows for the generation of data at varying sampling rates (up to a maximum of 30 samples per second) and ranging in length from seconds to years. The generative models are tested extensively to verify that they correctly capture the diverse characteristics of real loads. Finally, an opensource tool called LoadGAN is developed which gives researchers access to the fully trained generative models via a graphical interface.

 

In this study, a machine learning based method is proposed for creating synthetic eventful phasor measurement unit (PMU) data under time-varying load conditions. The proposed method leverages generative adversarial networks to create quasi-steady states for the power system under slowly-varying load conditions and incorporates a framework of neural ordinary differential equations (ODEs) to capture the transient behaviors of the system during voltage oscillation events. A numerical example of a large power grid suggests that this method can create realistic synthetic eventful PMU voltage measurements based on the associated real PMU data without any knowledge of the underlying nonlinear dynamic equations. The results demonstrate that the synthetic voltage measurements have the key characteristics of real system behavior on distinct time scales.