基于小波与最小资源分配网络的超短期风电功率预测研究  

Ultra-short-term wind power prediction based on wavelet and minimum resource allocation network

作　　者：杨杰 霍志红[1] 何永生 郭苏[1] 邱良 许昌[1] YANG Jie;HUO Zhihong;HE Yongsheng;GUO Su;QIU Liang;XU Chang(College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China;Power China Kunming Engineering Corporation Limited, Kunming 650051, China)

机构地区：[1]河海大学能源与电气学院,江苏南京211100 [2]中国电建昆明勘测设计研究院有限公司,云南昆明650051

出　　处：《电力系统保护与控制》2018年第9期55-61,共7页Power System Protection and Control

基　　金：中丹国际科技合作专项项目资助(2014DFG62530);国家自然科学基金项目资助(51507053);中央高校基本科研业务费项目-科技发展前瞻性研究专项资助(2017B42314)~~

摘　　要：针对风电场实际风速和风电功率序列的波动性、间歇性等特点以及RBF神经网络结构一旦确定隐节点个数就不可变等缺陷,提出了基于小波分析和最小资源分配网络的超短期风电功率预测方法。首先将历史风速和风电功率序列进行小波去噪及多频分解,得到多组高频信号和一组低频信号。然后对各频信号分别建立神经网络预测模型对未来4 h风电功率进行超短期预测。最后将各预测结果通过小波重构得到最终的超短期预测功率。实验结果证明,该方法能有效提高预测精度。Because the actual wind speed and wind power sequences are fluctuating, intermittent and the hidden node number of RBF neural network is unchangeable after the structure of RBF neural network is confirmed, a method of ultra-short-term wind power prediction based on wavelet and minimum resource allocation network is proposed. Firstly the historical wind speed and wind power sequences are denoised and multi frequency decomposed by wavelet transform, several high frequency signals and a low frequency signals are obtained. Then neural network prediction models of different frequency signals are built respectively to predict the wind power in the next 4 hours. Finally, the final ultra-short-term wind power prediction result is obtained from wavelet reconstruction of different components. The experimental results show that this method can effectively improve the prediction accuracy.

关 键 词：风电场 神经网络 小波分析 最小资源分配网络 超短期风电功率预测 

分 类 号：TM614[电气工程—电力系统及自动化]