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

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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
- Issue 11, Pages: 623-628(2025)
- 作者机构：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1150
  CLC： K83
- Published Online：04 December 2025，
  
  Published：04 December 2025
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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：

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

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