Ultra-short-term wind forecast of the wind farm based on VMD-BiGRU

Abstract

The ultra-short-term forecast of wind conditions is mainly concentrated in the forecast range of a few minutes and has an important guiding role in wind power system dispatching, wind turbine control, and wind power load tracking. Due to the characteristics of sudden change, non-stationarity, and volatility of short-term wind direction and wind speed, these random and volatile properties bring great difficulties to the prediction of ultra-short-term wind conditions. The current research only predicts a single wind speed or wind direction and does not predict both at the same time, which also brings certain limitations to the dispatching of wind power systems. Given the above characteristics of wind speed and wind direction, the decomposition method can be used to divide it into multi-scale components, thereby reducing the complexity of the original signal, increasing the stability of the signal, and improving the accuracy of prediction. Therefore, this paper uses the VMD decomposition method to decompose the original wind direction and wind speed data constructs multi-scale prediction features, and explores the laws of each component. The bi-directional GRU model has a strong ability to capture the sequence fluctuation law, and the decomposed modal components are input into the bi-directional GRU model to predict the wind speed. Through a large number of experiments and the comparison of different methods, it is shown that the VMD-BiGRU-based model has high prediction accuracy, small error, and higher efficiency in wind direction and wind speed prediction.