Prediction of wind power generation base on neural network in consideration of the fault time

Abstract

Along with the rapid increase in wind power penetration into the power grid, wind power generation predicting is becoming increasingly important to power system operators and electricity market participants. However, the random nature of the wind power would increase the uncertainty of power systems. The influencing factor is one of the most important factors in the quality of wind power prediction. In order to obtain a higher prediction accuracy, a two‐stage prediction method combined with meteorological factor and fault time is proposed. In the first stage, we present a detailed review of fault time of wind power predicting with machine‐learning methods and compare ten different models based on different influencing factors, and then, the second stage is combined with the predicted results of first stage, coupled with meteorological factors for the final wind power prediction. The results show good prediction accuracy in operation data of one wind farm from the Hu‐Nan province with different machine‐learning algorithms. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.