Abstract: Volatility and randomness of wind power,especially power ramping events,seriously threaten the sound and stable operation of the power grid. Ramping events,which accidentally,are generated under the influence of extreme weather. It’s extremely low probability of occurrence leads to a huge shortage of historical data,restricting the prediction accuracy of traditional power prediction models. To handle such problems,a wind power ramping prediction model based on generative adversarial network is proposed. Taking historical ramping data and simulated feature quantities as input,through the adversarial training of the generator and discriminator,a large number of simulated data with similar characteristics to the historical samples are generated to realize the expansion of the ramping data set. Then the expanded data set is used as input to the long and short-term memory neural network algorithm to predict ramping power. The simulation results show that the method is effective in predicting wind power climbing power in the case of lack of historical climbing data. And compared with the traditional prediction method, the accuracy of the prediction is proved.