Grid Defect Data Completion Based on Generative Adversarial Imputation Nets

With the in-depth development of grid intelligence, the data in the power system becomes more and more important. However, in the environment of high magnetoelectric interference in the power system, data collection and transmission are missing due to equipment failure and communication interference. In this case, it is necessary to complete the data processing. The traditional reconstruction method ignores the inherent relationship between the data and the system, which reduces the accuracy. This paper proposes a method of power system data completion based on Generative Adversarial Imputation Nets. This article first introduces the characteristics of GAN and power system missing data to prepare for data completion; then introduces the Generative Adversarial Imputation Nets algorithm; finally designs the data completion algorithm of the power system to realize the data completion function. The feasibility of the algorithm is verified on the python platform.