Short-term wind farm cluster power prediction based on dual feature extraction and quadratic decomposition aggregation

Abstract

The intermittency and uncertainty of wind energy affect the accuracy of wind power prediction, which is not conducive to the safe and stable operation of the power system. Aiming at this problem, a short-term power prediction method for multi-Stacking wind power clusters based on dual feature extraction and quadratic decomposition aggregation is proposed. Firstly, a graph network matrix is built based on the spatial relationship of wind farm clusters to extract spatial features, and then a subset of features is filtered using a feature selection method combining filter and wrapper. The original signal is then decomposed using a quadratic decomposition method combining variational modal decomposition and swarm decomposition to reduce the volatility of the signal; the wind power signals are reconstructed using multiscale permutation entropy, and finally the reconstructed signals are fed into multi-Stacking models for information modelling depending on the frequency to predict rolling multi-step power of wind farm clusters. A real wind farm cluster arithmetic example is used for validation. The experimental results show that NRMSE, NMAE, and MAPE are improved by 15.8 %, 8.99 %, and 6.79 % compared with the traditional model. The method has important reference value for both grid scheduling and stable operation.