谐波激励下风机塔筒模态参数自动识别

杨春侠; 陈涛; 张永涛; 万鹏; 吴钧

doi:10.3724/j.gyjzG23080713

谐波激励下风机塔筒模态参数自动识别

doi: 10.3724/j.gyjzG23080713

长沙理工大学土木工程学院, 长沙 410114

基金项目:

国家自然科学基金项目（51678067）。

详细信息

作者简介:
杨春侠，博士，副教授，主要从事结构可靠度、结构健康监测研究。

通讯作者:
陈涛，硕士研究生，主要从事结构健康监测研究，chentao9597@163.com。

计量
- 文章访问数: 31
- HTML全文浏览量: 1
- PDF下载量: 0
- 被引次数: 0
出版历程
- 收稿日期: 2023-08-07
- 网络出版日期: 2024-05-29

Automatic Identification of Modal Parameters of Wind Turbine Towers Under Harmonic Excitation

School of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, China

摘要

摘要: 运行工况下风力发电机组的周期性激励会产生与结构模态频率相近的谐波干扰，将影响结构基本模态的振动水平及动力参数的识别。为了在运行过程中有效连续监测塔筒振动状态，提出了一种基于势能凝聚层次聚类（PHA）的协方差驱动随机子空间法（Cov-SSI）与概率密度函数法（PDF）相结合的风机塔筒模态参数自动识别方法。通过现场振动响应测试，首先采用Cov-SSI法初步识别塔筒结构模态参数；其次，引入PHA法改进稳定图，定义频率与模态置信准则（MAC）距离矩阵进行清洗、聚类，实现自动化分离不同阶模态；最后将聚类簇信息通过PDF法判定并剔除谐波模态。结果表明：所提方法能有效分离并剔除谐波分量，实现风机塔筒运行模态参数的自动化识别，对风电机组安全运行自动化实时监测具有较好的工程应用价值。
- 风机塔筒 /
- 振动 /
- 谐波 /
- 势能凝聚层次聚类 /
- 模态参数
Abstract: The periodic excitation of wind turbines under operating conditions will generate harmonic disturbances with similar frequencies to the structural modes, which will affect the vibration level of the fundamental modes of the structure and the identification of the dynamic parameters. In order to effectively and continuously monitor the tower vibration status during operation, the covariance-driven stochastic subspace identification (Cov-SSI) method based on potential hierarchical agglomerative clustering (PHA) combined with the probability density function (PDF) was proposed for the automatic identification of modal parameters of wind turbine towers. Through the on-site vibration response test, the Cov-SSI method was firstly used to initially identify the tower structure modal parameters; secondly, the PHA method was introduced to improve the stability diagram, and the frequency and modal confidence criterion (MAC) distance matrix was defined for cleaning and clustering to automate the separation of different orders of modes; finally, the information of the clustered clusters was used to determine and eliminate the harmonic modes by the PDF method. The results showed that the proposed method could effectively separate and eliminate the harmonic components, realize the automatic identification of the modal parameters of wind turbine towers under operation, and provide a good engineering application value for the automated real-time monitoring of wind turbine safety operation.
- wind turbine tower /
- vibration /
- harmonics /
- potential hierarchical agglomerative clustering /
- modal parameters

HTML全文

参考文献(22)

[1]	DEVRIENDT C, MAGALHÃES F, WEIJTJENS W, et al. Structural health monitoring of offshore wind turbines using automated operational modal analysis[J]. Structural Health Monitoring, 2014, 13(6):644-659.
[2]	DONG X, LIAN J, WANG H. Vibration source identification of sffshore wind turbine structure based on optimized spectral kurtosis and ensemble empirical mode decomposition[J]. Ocean Engineering, 2019, 172:199-212.
[3]	魏煜锋,何维令,蒋祥增,等.高柔塔风电机组塔筒振源特性分析[J].振动与冲击, 2023, 42(2):189-196.
[4]	刘宇飞,辛克贵,樊健生,等.环境激励下结构模态参数识别方法综述[J].工程力学, 2014, 31(4):46-53.
[5]	CARNE T G, DOHRMANN C R. A model test design strategy for model correlation[C]//International Modal Analysis Conference. Nashville, United States:1995.
[6]	CARNE T G, JAMES G H. The inception of OMA in the development of modal testing technology for wind turbines[J]. Mechanical Systems and Signal Processing, 2010, 24(5):1213-1226.
[7]	JAMES G H, CARNE T G, LAUFFER J P. The natural excitation technique (NExT) for modal parameter extraction from operating wind turbines[J]. The International Journal of Analytical and Experimental Modal Analysis, 1995, 10:260-277.
[8]	董霄峰,练继建,杨敏,等.谐波干扰下海上风机结构工作模态识别[J].振动与冲击, 2015, 34(10):152-156 , 172.
[9]	HU W, THÖNS S, ROHRMANN G R, et al. Vibration-based structural health monitoring of a wind turbine system. part I:resonance phenomenon[J]. Engineering Structures, 2015, 89:260-272.
[10]	PEETERS B. System Identification and damage detection in civil engineering[D]. Leuven:Catholic University Leuven, Department of Civil Engineering, 2000.
[11]	WAGNER G, LIMA R, SAMPAIO R. Modal identification of a light and flexible wind turbine blade under wind excitation[J]. Journal of Engineering Mathematics, 2022, 133(1):1-15.
[12]	赵艳,郑卫锋,王新武,等.环境激励下风机结构模态识别算法的对比[J].噪声与振动控制, 2022, 42(4):64-68 , 92.
[13]	DONG X, LIAN J, YANG M,et al. Operational modal identification of offshore wind turbine structure based on modified stochastic subspace identification method considering harmonic interference[J]. Journal of Renewable&Sustainable Energy, 2014, 6(3):1649-1654.
[14]	MAGALHÃES F,ÁLVARO C, CAETANO E. Online automatic identification of the modal parameters of a long span arch bridge[J]. Mechanical Systems&Signal Processing, 2009, 23(2):316-329.
[15]	郑沛娟,林迪南,宗周红,等.基于图论聚类的随机子空间模态参数自动识别[J].东南大学学报(自然科学版), 2017, 47(4):710-716.
[16]	张永祥,刘心,褚志刚,等.基于随机子空间法的模态参数自动提取[J].机械工程学报, 2018, 54(9):187-194.
[17]	尹红燕,刘东霞,唐莉.基于K-means聚类算法的桥梁结构真实模态筛选研究[J].公路交通科技, 2020, 37(5):73-82.
[18]	梁鹏,贺敏,张阳,等.实时在线桥梁模态参数自动识别[J].振动,测试与诊断, 2021, 41(1):76-84 , 201.
[19]	LU Y, WAN Y. PHA:a fast potential-based hierarchical agglomerative clustering method[J]. Pattern Recognition, 2013, 46(5):1227-1239.
[20]	PARZEN E. On estimation of probability density function and mode[J]. The Annals of Mathematical Statistics, 1962, 33(3):1065-1076.
[21]	AUWERAER H V D, PEETERS B. Discriminating physical poles from mathematical poles in high order systems:use and automation of the stabilization diagram[C]//Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference. Como, Italy:2004.
[22]	徐健,周志祥,唐亮,等.基于谱系聚类分析的桥梁结构模态参数自动化识别方法研究[J].振动与冲击, 2017, 36(11):206-214.