Due to the randomness and volatility of new energy data，data quality problems such as lack of data，repetition，abnormality and uneven distribution of grid-connected data have become more and more prominent. Studies on data quality evaluation and governance have important and positive significance for the development of new energy. Traditional data quality research methods are not suitable for solving new energy data quality problems，while artificial intelligence algorithms have incomparable advantages in dealing with this problem. Generative adversarial networks （GAN） is one of the hottest research directions in the field of artificial intelligence in recent years，and its excellent data generation ability has attracted wide attention. Firstly，this paper introduces the framework，advantages，disadvantages and improvement of classic GAN. Then，the application of GAN in new energy，the research literature of new energy data quality and GAN applying in new energy data quality are reviewed. Finally，this paper summarizes the paper and looks forward to the possible application of GAN in new energy.