基于卷积神经网络的风电机组气动不平衡故障诊断方法研究

杨旺春; 梁雪; 孙传宗

doi:10.19912/j.0254-0096.tynxb.2023-1764

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 >

基于卷积神经网络的风电机组气动不平衡故障诊断方法研究

更新时间：2025-10-18

- 基于卷积神经网络的风电机组气动不平衡故障诊断方法研究
- STUDY ON DETECTION METHOD OF ROTOR AERODYNAMIC IMBALANCE BASED ON CONVOLUTIONAL NEURAL NETWORK
- 太阳能学报 2025年46卷第3期页码：531-537
- 作者机构：
  
  1. 国电电力广西风电开发有限公司,北海 536000
  2. 辽宁中科经创智慧能源有限公司,沈阳 110027
  3. 沈阳工业大学机械工程学院,沈阳 110870
- 作者简介：
- 基金信息：
  
  辽宁省科技厅博士启动基金（2019-BS-182）;航空发动机双转子系统非线性动力学建模及振动机理研究
- DOI：10.19912/j.0254-0096.tynxb.2023-1764
  中图分类号： TK81
- 网络出版：2025-04-07，
  
  纸质出版：2025
- 稿件说明：
移动端阅览
杨旺春, 梁雪, 孙传宗. 基于卷积神经网络的风电机组气动不平衡故障诊断方法研究[J]. 太阳能学报, 2025, 46(3): 531-537.

Yang Wangchun, Liang Xue, Sun Chuanzong. STUDY ON DETECTION METHOD OF ROTOR AERODYNAMIC IMBALANCE BASED ON CONVOLUTIONAL NEURAL NETWORK[J]. 2025, 46(3): 531-537.
杨旺春, 梁雪, 孙传宗. 基于卷积神经网络的风电机组气动不平衡故障诊断方法研究[J]. 太阳能学报, 2025, 46(3): 531-537. DOI： 10.19912/j.0254-0096.tynxb.2023-1764.

Yang Wangchun, Liang Xue, Sun Chuanzong. STUDY ON DETECTION METHOD OF ROTOR AERODYNAMIC IMBALANCE BASED ON CONVOLUTIONAL NEURAL NETWORK[J]. 2025, 46(3): 531-537. DOI： 10.19912/j.0254-0096.tynxb.2023-1764.

摘要

为解决风电机组中风轮气动不平衡的诊断问题

降低风电机组的运维成本

提出一种基于一维卷积神经网络的风轮不平衡识别方法。融合变分模态分解和相关峭度计算实现风轮气动不平衡的感知。并提出基于一维卷积神经网络的气动不平衡识别方法

以机舱的振动加速度作为输入

识别气动不平衡的具体程度。在不同湍流强度和噪声环境下进行交叉验证

识别结果的准确率在95%以上

证明该方法可应用于风轮不平衡的诊断中

提升风电机组运行的安全性。

Abstract

For the problem of identification for rotor imbalance in wind turbine

and to reduce the operation and maintenance cost of wind turbine

an identification method of rotor imbalance based on one-dimensional convolutional neural network is proposed. Firstly

the combination of variational mode decomposition （VMD） and correlation kurtosis calculation is used to realize the perception of the rotor aerodynamic imbalance. Secondly

a recognition method of aerodynamic imbalance based on one-dimensional convolutional neural network is proposed

and the vibration acceleration of the nacelle is taken as the input to identify the specific magnitude of the rotor aerodynamic imbalance. Finally cross-validation was performed in different turbulence intensity and noise environments

and the identification accuracy of the cross validation was more than 95%

which proved that the method could be applied to the diagnosis of rotor imbalance and improve the safety of wind turbine.

关键词

Keywords

references

XING Z X, CHEN M Y, CUI J, et al.Detection of magnitude and position of rotor aerodynamic imbalance of wind turbines using Convolutional Neural Network[J]. Renewable energy, 2022, 197: 1020-1033.

赵凯. 风轮不平衡特性分析与故障诊断方法研究[D]. 沈阳: 沈阳工业大学, 2019.

NIEBSCH J, RAMLAU R, NGUYEN T T.Mass and aerodynamic imbalance estimates of wind turbines[J]. Energies, 2010, 3(4): 696-710.

万书亭, 程侃如, 绳晓玲, 等. 基于风速时空分布的双馈风力发电机组叶轮质量不平衡故障特性分析[J]. 太阳能学报, 2021, 42(9): 236-243.

巫发明, 杨从新, 王清, 等. 大型风力机风轮气动不平衡的特性研究与验证[J]. 太阳能学报, 2021,42(1):192-197.

CACCIOLA S, AGUD I M, BOTTASSO C L.Detection of rotor imbalance, including root cause, severity and location[J]. Journal of physics: conference series, 2016, 753: 072003.

HÜBNER G R, PINHEIRO H, DE SOUZA C E, et al. Detection of mass imbalance in the rotor of wind turbines using Support Vector Machine[J]. Renewable energy, 2021, 170: 49-59.

董健, 柳亦兵, 滕伟, 等. 基于BP-Adaboost算法的风电机组叶片结冰检测[J]. 可再生能源, 2021,39(5):632-636.

RAMLAU R, NIEBSCH J. Imbalance estimation without test masses for wind turbines [J]. Journal of solar energy engineering, 2009, 131(1): 011010-.

MCDONALD G L, ZHAO Q, ZUO M J.Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection[J]. Mechanical systems and signal processing, 2012, 33: 237-255.

张伟. 基于卷积神经网络的轴承故障诊断算法研究[D]. 哈尔滨: 哈尔滨工业大学, 2017.

MYRENT N J, BILAL N, ADAMS D, et al. Aerodynamic sensitivity analysis of rotor imbalance and shear web disbond detection strategies for offshore structural health prognostics management of wind turbine blades[C]//32nd ASME Wind Energy Symposium. National Harbor, Maryland, 2014: AIAA2014-0714.

MALIK H, MISHRA S.Application of probabilistic neural network in fault diagnosis of wind turbine using fast, turbsim and Simulink[J]. Procedia computer science, 2015,58: 186-193.

赵丽军, 张璠, 厉伟, 等. 风电机组风轮不平衡机械载荷时域特性分析[J]. 太阳能学报, 2020, 41(8):342-350.

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

并行卷积神经网络的风电机组故障诊断方法

基于迁移学习的风电机组轴承故障诊断研究

基于支持向量机的风轮不平衡故障诊断方法研究