基于小波变换与IAGA-BP神经网络的短期风电功率预测

为提高风功率预测精度,减轻输出风能波动性对风电并网不利影响,提出了基于WT-IAGA-BP神经网络的短期风电功率预测方法。首先,利用风速分区、3σ准则及拉格朗日插值法清洗风电场历史数据；其次,依据小波重构误差,选择db4小波分别提取风速、风向、历史风功率的不同频率特征信号,并引入改进自适应遗传算法(IAGA)对各序列BP神经网络的初始权值与阈值寻优,使用Sigmiod函数通过适应度值自适应改变交叉概率与变异概率；最后,构建各序列的WT-IAGA-BP模型对短期风功率组合预测。通过仿真分析,并与ELM、WT-ELM及IAGA-BP方法对比,验证该方法具有更高的预测精度和更好的预测性能。

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.