孙国良,伊力哈木.亚尔买买提,张宽,吐松江.卡日,李振恩,邸强.基于小波变换与IAGA-BP神经网络的短期风电功率预测[J].电测与仪表,2024,61(5):126-134. Sun Guoliang,Yilihamu Yaermaimaiti,Zhang Kuan,Tusongjiang.Kari,Li Zhen-En,Di Qiang.Short-term prediction of wind power based on WT-IAGA-BPNN[J].Electrical Measurement & Instrumentation,2024,61(5):126-134.
基于小波变换与IAGA-BP神经网络的短期风电功率预测
Short-term prediction of wind power based on WT-IAGA-BPNN
In order to improve the accuracy of wind power prediction and reduce the adverse impact of the fluctuation of output wind energy on wind power integration, a short-term wind power prediction method based on WT-IAGA-BP neural network is proposed. Firstly, data clean technologies, including wind speed partition, 3σ criterion and Lagrange interpolation method, are applied to remove abnormal values from the historical data of wind farm. Secondly, according to the wavelet reconstruction error, db4 wavelet transform (WT) is used to extract the different frequency characteristic signals of wind speed, wind direction and historical wind power respectively. Then, the improved adaptive genetic algorithm (IAGA) is introduced to obtain the optimized values for initial weights and thresholds of the BP neural network of each sequence, and sigmoid function is used to adaptively change the crossover probability and mutation probability through the fitness value. Finally, the WT-IAGA-BP model of each sequence is constructed to predict the short-term wind power combination. According to the simulation analysis and comparison with other prediction models including ELM、WT-ELM and IAGA-BP, the obtained results suggest that the presented prediction model has better prediction performance and higher prediction accuracy.