一种改进组合神经网络的超短期风速预测方法研究  

Research on an Ultra-Short-Term Wind Speed Prediction Method Based on Improved Combined Neural Networks

作　　者：邵宜祥 刘剑 胡丽萍 过亮 方渊 李睿 SHAO Yixiang;LIU Jian;HU Liping;GUO Liang;FANG Yuan;LI Rui(NARI Group Corporation,Nanjing 211106,Jiangsu Province,China)

机构地区：[1]南瑞集团有限公司,江苏省南京市211106

出　　处：《发电技术》2024年第2期323-330,共8页Power Generation Technology

基　　金：国家电网公司科技项目(524608140152)。

摘　　要：超短期风速预测是保障风电机组桨距角前馈控制实施效果的关键,对提高风电机组环境适应性具有重要影响。为了提高预测精度,提出了一种改进组合神经网络的超短期风速预测方法。该方法选择适合时间序列预测且具有较强非线性学习能力的BP神经网络和长短期记忆(long short-term memory,LSTM)神经网络进行加权组合,以消除单个神经网络可能存在的较大误差;同时,为了提高组合效果,采用差分进化算法对组合权重进行优化。将该方法应用于某风场超短期风速预测中,通过与单神经网络预测、等权重组合神经网络预测的结果对比,验证了所提方法在提高预测精度上的有效性。Ultra-short-term wind speed prediction is the key to ensure the implementation effect of wind turbine pitch angle feedforward control,and has an important impact on improving the environmental adaptability of wind turbines.In order to improve the prediction accuracy,an ultra-short-term wind speed prediction method based on an improved combined neural networks was proposed.In this method,BP neural network and long short-term memory(LSTM) neural network,which are suitable for time series prediction and have strong nonlinear learning ability,are selected for weighted combination to eliminate the large error that may exist in a single neural network.At the same time,to improve the combination effect,the differential evolution(DE)algorithm was used to optimize the combination weight.The method was applied to the ultra-short-term wind speed prediction of a wind farm.Compared with the results of single neural network prediction and equal weight combined neural networks prediction,the effectiveness of the proposed method in improving the prediction accuracy was verified.

关 键 词：风力发电 超短期风速预测 BP神经网络 长短期记忆(LSTM)神经网络 差分进化(DE)算法 

分 类 号：TK81[动力工程及工程热物理—流体机械及工程]