风资源评估测风数据插补方法的时空特性研究

马雪韵; 杨德成; 孟仁杰

doi:doi:10.19912/j.0254-0096.tynxb.2024-1525

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

风资源评估测风数据插补方法的时空特性研究

更新时间：2026-02-06

- 风资源评估测风数据插补方法的时空特性研究
- 风资源评估测风数据插补方法的时空特性研究
- 太阳能学报 2026年47卷第1期页码：494-501
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1525
  中图分类号： TK81
- 网络首发：2026-02-05，
  
  纸质出版：2026-02-05
- 稿件说明：
移动端阅览
马雪韵, 杨德成, 孟仁杰. 风资源评估测风数据插补方法的时空特性研究[J]. 太阳能学报, 2026,47(1):494-501.

马雪韵, 杨德成, 孟仁杰. 风资源评估测风数据插补方法的时空特性研究[J]. 2026, 47(1): 494-501.
马雪韵, 杨德成, 孟仁杰. 风资源评估测风数据插补方法的时空特性研究[J]. 太阳能学报, 2026,47(1):494-501. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1525.

马雪韵, 杨德成, 孟仁杰. 风资源评估测风数据插补方法的时空特性研究[J]. 2026, 47(1): 494-501. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1525.

摘要

为解决采用短期测风数据开展资源评估的可靠性、准确性、适用性难题

针对场内已立测风塔测风时长无法满足一个完整年及拟立测风塔如何选取合理测风时长

开展短期测风数据评估的应用场景探究测风数据插补方法的时空特性。在不同地形分类下通过大量数据处理模拟实际测风情况

采用插补方法延长数据

以插补数据均方误差（MSE）为主要评价指标

与真实数据进行对比分析

提出不同地形气候区域下不同测风时长的最优插补方法及拟立测风塔时测风时长推荐。

Abstract

During the pre-development period of wind power projects

timely and accurate assessment of site wind energy resources plays a key role in deciding a wind power project’s success or failure. In order to solve the problem that using a short-term wind data to conduct resource assessment may lead to unreliability

inaccuracy and inapplicability

in this study

we do research on the spatiotemporal characteristics of interpolation methods for measured wind data in two scenarios： an anemometer tower has been established but the measurement duration is less than 1 year; and the anemometer tower has not been established so the installation time and measuremert duration need to be decided. Across different terrain classification

data processing is repeatedly used to simulate real wind measurement. With different interpolation methods applied to predict data

the MSE between the interpolated predicted data and real wind data as the main assessment criterion

the study presents the recommended interpolation method in different terrain classifications and durations

and the recommend measurement duration in different terrain classifications.

关键词

Keywords

references

B/T 18710—2002, 风电场风能资源评估方法[S]. GB/T 18710—2002, Methodology of wind energy resource assessment for wind farm[S].

杨靖文,张静. 不同MCP方法在平原及山地风电项目中的插补应用[J]. 风能, 2020(6): 60-66.YANG J W,ZHANG J. Interpolation application of different MCP methods in plain and mountain wind power projects[J]. Wind energy, 2020(6): 60-66.

叶林, 杨丹萍, 赵永宁. 风电场风能资源评估的测量-关联-预测方法综述[J]. 电力系统自动化, 2016, 40(3):140-151.YE L, YANG D P, ZHAO Y N. Overview of wind resource measure-correlate-predict methods for wind farms[J]. Automation of electric power systems, 2016, 40(3): 140-151.

ANDERSON M.A review of MCP techniques[R] Kingsland, UK: Renewable energy systems Ltd., 2004.

蒋晨, 缪书唯. 基于TOPSIS法的风速概率分布评价方法[J]. 太阳能学报, 2023, 44(8): 499-508.JIANG C, MIAO S W. Evaluation method for wind speed probability distribution based on TOPSIS method[J]. Acta energiae solaris sinica, 2023, 44(8): 499-508.

马偲征, 王聪, 王小荣, 等. 基于混合深度学习模型的风速区间预测研究[J]. 太阳能学报, 2023, 44(3): 139-146.MA C Z, WANG C, WANG X R, et al. Research on wind speed interval prediction based on hybrid deep learning model[J]. Acta energiae solaris sinica, 2023, 44(3): 139-146.

李炳元,潘保田,韩嘉福等. 中国陆地基本地貌类型及其划分指标探讨[J]. 第四纪研究, 2008, 28(4): 535-543.LI B Y,PAN B T,HAN J F,et al. Basic terrestrial geomorphological types in China and their circumscriptions[J]. Quaternary sciences, 2008, 28(4): 535-543.

于佳鹤,崔杰,王风涛. 风电场测风数据插补方法研究及插补结果偏差分析[J]. 太阳能, 2021(32): 26-35.YU J H,CUI J,WANG F T. Research on interpolation method of wind measurement data of wind farm and analysis of interpolation results deviation[J]. Solar energy, 2021(32):26-35.

ROGERS A L, ROGERS J W, MANWELL J F.Comparison of the performance of four measure-correlate-predict algorithms[J]. Journal of wind engineering and industrial aerodynamics, 2005, 93(3): 243-264.

KING C, HURLEY B.The SpeedSort, DynaSort and scatter wind correlation methods[J]. Wind engineering, 2005, 29(3): 217-241.

LEBLANC M,SCHOBORG D,COX S,et al.Is a non-linear MCP method a useful tool for North American wind regimes[C]//Proceedings of the WINDPOWER 2009 Conference. Chicago, Illinois, USA, 2009.

LAMBERT T,GRUE A.The matrix times series method for MCP[C]//Proceedings of the WINDPOWER 2012 Conference. Atlanta, Georgia, USA, 2012.

何春雄, 龙卫江, 朱锋峰. 概率论与数理统计[M]. 2版. 北京: 高等教育出版社, 2012.HE C X,LONG W J,ZHU F F. Probability theory and mathematical statistics[M]. 2nd edition. Beijing: Higher Education Press, 2012.

吴喜之. 统计学:从数据到结论[M]. 3版. 北京:中国统计出版社, 2006.WU X Z. Statistics: from data to conclusions[M]. 3rd edition. Beijing: China Statistics Press, 2006.

BLACK K.Business statistics: contemporary decision making[M]. 10th edition. Hoboken: John Wiley and Sons, 2019.

贾俊平, 何晓群, 金勇进. 统计学[M]. 4版. 北京:中国人民大学出版社, 2009.JIA J P,HE X Q,JIN Y J. Statistics[M]. 4th edition. Beijing: China Renmin University Press, 2009.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

5套再分析资料在阿勒泰地区风资源评估中的适用性研究

基于WRF模式的中长期风速预报及订正研究

优化变分模态分解下NRBO-LSTM-Attention修正预测风速的风电功率短期预测

基于机器学习方法的风速偏差订正研究

关于最佳风场和风轮尺寸的研究