A PV generation data reconstruction method based on improved super-resolution generative adversarial network

Abstract

With the growing penetration of solar photovoltaic (PV) generation, advanced data analysis methods have been applied to the smart grid operation. However, the low-temporal-resolution PV generation data limits the utilization of the data analysis methods, because the low-temporal-resolution PV generation data contains too little information. On the other hand, the existing data reconstruction methods are less than satisfactory in reconstructing high-temporal-resolution PV generation data from low-temporal-resolution data, since most of them cannot fully capture the characteristics of PV generation data. To address this issue, a PV generation data reconstruction method based on improved super-resolution generative adversarial network is proposed in this paper. First, a data-image construction method is proposed to encode the PV generation data into the so-called data-images. Furthermore, we develop a data-image super-resolution generative adversarial network (DISRGAN) model, and the data-images are used to train the DISRGAN model. Finally, based on the trained DISRGAN model, a general framework is developed to reconstruct high-temporal-resolution PV generation data from low-temporal-resolution data. Numerical experiments have been carried out based on PV generation data from the State Grid Corporation of China, to reconstruct the high-temporal-resolution data from low-temporal-resolution data. The results demonstrate the superior performance of the proposed framework compared with a series of state-of-the-art methods.