考虑尾流效应的风力机疲劳载荷代理模型研究

王彬滨; 彭留留; 黄国庆; 杨小龙; 刘伟杰; 信志强

doi:doi:10.19912/j.0254-0096.tynxb.2024-1150

欢迎来到能源电力期刊网！

English Version 读者登录期刊登录

您当前的位置：

首页 >

文章列表页 >

考虑尾流效应的风力机疲劳载荷代理模型研究

更新时间：2025-12-10

- 考虑尾流效应的风力机疲劳载荷代理模型研究
- 考虑尾流效应的风力机疲劳载荷代理模型研究
- 太阳能学报 2025年第11期页码：623-628
- 作者机构：
- 作者简介：
- 基金信息：
  
  国家自然科学基金面上项目（52378480）;重庆市自然科学基金创新发展联合基金（CSTB2024NSCQ-LZX0010）;学科创新引智基地项目（B18062）;中央高校基本科研业务费项目（2022CDJQY-009）
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1150
  中图分类号： K83
- 网络出版：2025-12-04，
  
  纸质出版：2025-12-04
- 稿件说明：
移动端阅览
王彬滨, 彭留留, 黄国庆, 等. 考虑尾流效应的风力机疲劳载荷代理模型研究[J]. 太阳能学报, 2025,(11):623-628.

王彬滨, 彭留留, 黄国庆, et al. 考虑尾流效应的风力机疲劳载荷代理模型研究[J]. 2025, (11): 623-628.
王彬滨, 彭留留, 黄国庆, 等. 考虑尾流效应的风力机疲劳载荷代理模型研究[J]. 太阳能学报, 2025,(11):623-628. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1150.

王彬滨, 彭留留, 黄国庆, et al. 考虑尾流效应的风力机疲劳载荷代理模型研究[J]. 2025, (11): 623-628. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1150.

摘要

该文提出一种能够考虑尾流效应的风力机疲劳载荷代理模型。首先

根据工程经验选取合理的模型输入参数样本。其次

采用TurbSim生成相应的入流风场文件

并利用FAST.Farm软件开展双风力机载荷仿真

得到尾流区和非尾流区的风力机疲劳载荷数据库。最后

采用支持向量回归和反向传播神经网络分别建立尾流区和非尾流区的风力机疲劳载荷代理模型。结果表明

相较于传统风力机疲劳载荷代理模型

该文提出的包含风力机间距输入参数的风力机疲劳载荷代理模型能够考虑风力机间的尾流效应

此建模方式更加合理。此外

综合考虑归一化均方根误差（NRMSE）和回归系数（R2）两项模型评价指标

支持向量回归的建模效果优于反向传播神经网络

且模型在叶片和塔顶部位的预测效果普遍好于塔基。

Abstract

A wind turbine fatigue load surrogate model that can account for wake effects is proposed in this paper. First

reasonable samples of input parameters for the model are selected based on engineering experience. Then

TurbSim is used to generate corresponding inflow wind field files

and the FAST. Farm software is used to carry out simulations of dual wind turbine loads to obtain a database of fatigue loads in wake and non-wake regions. Finally

support vector regression （SVR） and backpropagation neural networks （BPNN） are used to establish wind turbine fatigue load surrogate models for wake and non-wake regions

respectively. The results show that compared with traditional wind turbine fatigue load surrogate models

the proposed model

which includes wind turbine spacing as an input parameter

can consider wake effects between turbines

making the modeling approach more reasonable. Furthermore

considering both ² and NRMSE evaluation metrics

the modeling performance of SVM is superior to that of BPNN

with the models generally predicting loads on blades and the tower-top more accurately than at the tower base.

关键词

Keywords

references

KAMAL M, RAHMAN M M.Advances in fatigue life modeling: a review[J]. Renewable and sustainable energy reviews, 2018, 82: 940-949.

Wind turbine generator systems-Part 1: safety requirement: IEC 61400-1 Ed. 2.0 en: 1999 systems-Part 1: safety requirement: IEC 61400-1 Ed. 2.0 en: 1999[S]. International Electrotechnical Commission, 1999.

刘伟杰, 黄国庆, 彭留留, 等. 考虑风力机疲劳载荷限制的风电场布局优化研究[J]. 太阳能学报, 2025, 46(6):548-555.

DIMITROV N, KELLY M C, VIGNAROLI A, et al.From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases[J]. Wind energy science, 2018, 3(2): 767-790.

SCHRÖDER L, KRASIMIROV DIMITROV N, VERELST D R, et al. Wind turbine site-specific load estimation using artificial neural networks calibrated by means of high-fidelity load simulations[J]. Journal of physics: conference series, 2018, 1037(6): 062027.

GASPARIS G, LIO W H, MENG F Z.Surrogate models for wind turbine electrical power and fatigue loads in wind farm[J]. Energies, 2020, 13(23): 6360.

HAGHI R, CRAWFORD C.Data-driven surrogate model for wind turbine damage equivalent load[J]. Wind energy science, 2024, 9(11): 2039-2062.

牟哲岳, 孙勇, 王瑞良, 等. 基于实测数据和机器学习的风电机组载荷预测模型[J]. 太阳能学报, 2023, 44(10): 414-419.

王伟, 王海云. 基于Adam优化BP神经网络的风机等效疲劳载荷预测[J]. 现代电子技术, 2023, 46(17): 102-106.

黄国庆, 刘伟杰, 王彬滨, 等. 基于深度神经网络的风力机疲劳载荷代理模型研究[J]. 太阳能学报, 2025, 46(4): 398-405.

JONKMAN B J,BUHL M L.TurbSim user’s guide[R]. National Renewable Energy Laboratory, Technical Report Golden, Colorado, 2006.

JONKMAN J M,SHALER K.FAST.farm user’s guide and theory manual[M]. Golden, CO, USA: National Renewable Energy Laboratory, 2021.

JONKMAN J, BUTTERFIELD S, MUSIAL W, et al.Definition of a 5-MW reference wind turbine for offshore system development[J]. Contract, 2009(February): 1-75.

HAYMAN G J, BUHL J M.Mlife users guide for version 1.00[J]. National renewable energy laboratory, 2012,74(75): 112.

FRANDSEN S, BARTHELMIE R, PRYOR S, et al.Analytical modelling of wind speed deficit in large offshore wind farms[J]. Wind energy, 2006, 9(1/2): 39-53.

AWAD M, KHANNA R.Support vector regression[M]//Efficient Learning Machines. Berkeley, CA: Apress, 2015: 67-80.

HECHT R.Theory of the backpropagation neural network[M]. Neural networks for perception. Academic press, 1992, 1: 65-93.

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

考虑风力机疲劳载荷限制的风电场布局优化研究

基于深度神经网络的风力机疲劳载荷代理模型研究

串列不等直径水平轴风力机气动性能研究

风电场尾流控制中偏航角优化算法研究