基于风速波动特征提取的超短期风速预测

doi:10.19912/j.0254-0096.tynxb.2020-1371

太阳能学报 ›› 2022, Vol. 43 ›› Issue (9): 308-313.DOI: 10.19912/j.0254-0096.tynxb.2020-1371

基于风速波动特征提取的超短期风速预测

张家安¹, 刘东¹, 刘辉², 宋鹏², 刘京波², 吴宇辉²

1.河北工业大学人工智能与数据科学学院,天津 300401;
2.国网冀北电力有限公司电力科学研究院,北京 100045

收稿日期:2020-12-21 出版日期:2022-09-28 发布日期:2023-03-28
通讯作者: 张家安（1975—）,男,博士、讲师,主要从事新能源发电系统及智能化、新能源接入电网系统建模与仿真方面的研究。zhangjiaan@foxmail.com
基金资助:
河北省自然科学基金项目（E2020202142）

ULTRA SHORT TERM WIND SPEED PREDICTION BASED ON WIND SPEED FLUCTUATION FEATURE EXTRACTION

Zhang Jiaan¹, Liu Dong¹, Liu Hui², Song Peng², Liu Jingbo², Wu Yuhui²

1. College of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300401, China;
2. State Grid Jibei Electric Power Co., Ltd., Research Institute, North China Electric Power Research Institute Co., Ltd., Beijing 100045, China

Received:2020-12-21 Online:2022-09-28 Published:2023-03-28

摘要/Abstract

摘要： 针对风电场风速预测准确度不高的问题,提出一种基于风速波动特征提取的超短期风速预测方法。首先建立风速-风速变化量联合概率密度模型,分析风速的不确定性特征;根据风速波动特征,应用集合经验模态分解（EEMD）和风速分量样本熵（SampEn）值,将风速分解重组为波动量和趋势量;应用人工鱼群算法（AFSA）优化小波神经网络（WNN）进行趋势量预测;应用改进非线性自回归（INARX）神经网络对风速波动量进行预测,进而得到预测风速。通过实际风电场风速仿真预测,并与多种预测方法对比,表明该预测方法预测结果误差较小,可准确地进行超短期风速预测。

关键词: 风力发电, 概率密度函数, 神经网络, 风速预测

Abstract: Aiming at the problem of low accuracy of wind speed prediction in wind farms, an ultra-short term wind speed prediction method based on wind speed fluctuation feature extraction is proposed. Firstly, the joint probability density model of wind speed and wind speed variation is established to analyze the uncertainty characteristics of wind speed. According to the wind speed fluctuation characteristics, the wind speed decomposition is recombined into fluctuation and trend quantity by using the values of Ensemble Empirical Mode Decomposition （EEMD） and Sample Entropy （SampEn） of wind speed components. The Artificial Fish Swarm Algorithm （AFSA） is used to optimize the Wavelet?Neural?Network （WNN） for trend prediction. The Improved Non-linear Autoregression （INARX） with external input neural network is used to predict the wind speed fluctuation, and then the predicted wind speed is obtained. Through the actual wind farm wind speed simulation prediction and comparison with many forecasting methods, the results show that the prediction error of this method is low, and can accurately predict the ultra-short term wind speed.

Key words: wind power, probability density function, neural networks, wind speed forecasting

中图分类号:

TK18

张家安, 刘东, 刘辉, 宋鹏, 刘京波, 吴宇辉. 基于风速波动特征提取的超短期风速预测[J]. 太阳能学报, 2022, 43(9): 308-313.

Zhang Jiaan, Liu Dong, Liu Hui, Song Peng, Liu Jingbo, Wu Yuhui. ULTRA SHORT TERM WIND SPEED PREDICTION BASED ON WIND SPEED FLUCTUATION FEATURE EXTRACTION[J]. Acta Energiae Solaris Sinica, 2022, 43(9): 308-313.

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基于风速波动特征提取的超短期风速预测

ULTRA SHORT TERM WIND SPEED PREDICTION BASED ON WIND SPEED FLUCTUATION FEATURE EXTRACTION

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