Improved Generative Adversarial Network-Based Super Resolution Reconstruction for Low-Frequency Measurement of Smart Grid

Abstract

There is a universal trend toward a data-driven smart grid, which aims to realize two-way communication of energy flow and data flow between various agents across power generation side, transmissiondistribution side, electricity retailors and end users. However, the low frequency electrical measurement data accumulated over a long period of time is insignificant for intelligent agents. This paper presents a machine learning method for reconstructing the low frequency electrical measurement data in smart grid. Firstly, the electrical measurement data is converted into electrical images, and then the low frequency electrical measurement data is reconstructed into high frequency electrical measurement data by generative adversarial network to improve the training stability, Wasserstein distance is introduced into the reconstruction mechanism. In addition, by designing the deep residual network based generator, the deep convolutional network based discriminator as well as the perception loss function, the reconstruction accuracy and the high-frequency detail reduction ability are improved. The proposed method is tested on three publicly available datasets and compared with the traditional data reconstruction method, justifying that this method not only can restore high-frequency details with less error, but also can be generalized to different datasets at one location and to datasets at different locations with satisfactory accuracy.