基于Copula函数的叶片载荷相关性研究及预测分析

包道日娜, 高帆, 王鹏, 张少华, 韩峰

太阳能学报 ›› 2023, Vol. 44 ›› Issue (12) : 323-329.

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (12) : 323-329. DOI: 10.19912/j.0254-0096.tynxb.2023-0067

基于Copula函数的叶片载荷相关性研究及预测分析

  • 包道日娜1, 高帆1, 王鹏1, 张少华2, 韩峰3
作者信息 +

RESEARCH AND FORECASTING ANALYSIS OF BLADE LOAD CORRELATION BASED ON COPULA FUNCTION

  • Bao Daorina1, Gao Fan1, Wang Peng1, Zhang Shaohua2, Han Feng3
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文章历史 +

摘要

针对风速与叶片载荷的相关性,建立风速-载荷的Copula函数模型,提出一种基于Copula函数的预测方法并对叶片载荷进行预测。通过中小型变桨风力机风洞试验的载荷数据进行相关性建模并进行拟合优度检验,同时通过预测模型对载荷做出预测,采用显著性检验验证了预测效果的有效性。以1.5 kW风力机叶片载荷试验数据为例对所提方法验证,结果表明:在准确分析风速和叶片载荷相关性的基础上,采用Copula函数建立的预测模型能有效预测叶片载荷。

Abstract

In this paper, a Copula function model of wind speed-load is established for the correlation between wind speed and blade load, and a prediction method based on Copula function is proposed and the blade load is predicted. The correlation is modeled and the goodness-of-fit test is performed with the load data from wind tunnel tests of small and medium-sized variable pitch wind turbines, and the load is predicted by the prediction model, and the validity of the prediction effect is verified by the significance test. Take the 1.5 kW wind turbine blade load test data as an example to verify the proposed method. The results show that, based on the accurate analysis of wind speed and blade load correlation, the prediction model established by Copula function can effectively predict the blade load.

关键词

风力机 / 叶片 / 风速 / 相关性 / Copula函数 / 载荷预测

Key words

wind turbines / blades / wind speed / correlation / Copula function / load forecast

引用本文

导出引用
包道日娜, 高帆, 王鹏, 张少华, 韩峰. 基于Copula函数的叶片载荷相关性研究及预测分析[J]. 太阳能学报. 2023, 44(12): 323-329 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0067
Bao Daorina, Gao Fan, Wang Peng, Zhang Shaohua, Han Feng. RESEARCH AND FORECASTING ANALYSIS OF BLADE LOAD CORRELATION BASED ON COPULA FUNCTION[J]. Acta Energiae Solaris Sinica. 2023, 44(12): 323-329 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0067
中图分类号: TK83   

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基金

国家自然科学基金(52266013); 鄂尔多斯市科技合作重大专项(2021EEDSCXQDFZ009); 内蒙古自治区重点研发和成果转化计划(2022YFHH0048)

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