Prediction of Wind Power Ramp Events Based on Deep Neural Network

Wind power ramps seriously threaten grid security due to the increase in the proportion of wind power generation to total power generation. I,, order to improve the prediction accuracy of wind power ramp events, au Empirical Mode Decomposition Deep Neural Network algorithm (EMDDNN) is proposed First, the wind power data is preprocessed and the characteristics of the wind ramp event are analyzed Then, the appropriate time window is selected and the empirical modal decomposition of the time series data is used as the input variable of the prediction tnodel. Finally, a deep neural network is used to construct a wind power ramp prediction model. The experimental results based on actual production data show that the prediction accuracy of the EWAN algorithm proposed in this paper is higher than other comparison algorithms.