基于工作模态分析的风电机组叶片裂纹损伤在线监测研究

doi:10.11930/j.issn.1004-9649.202303035

摘要/Abstract

摘要：

针对风电机组叶片裂纹损伤发生概率高且难以发现的问题，通过叶片振动信号采集与分析来进行叶片裂纹损伤的在线监测。首先，基于工作模态分析理论构建了基于传递率的叶片模态参数在线识别方法，并搭建叶片振动物理实验台用于该方法的实验验证，通过与传统力锤激振法的实验结果对比，验证了该模态参数识别方法的准确性；然后，以某5 MW风电机组作为仿真算例，进行了叶片裂纹损伤故障仿真，并通过工作模态分析获取了损伤故障特征；最后，将叶片振动信号、模态参数和机组运行数据融合为多源数据集，结合LightGBM算法进行了叶片裂纹损伤故障诊断，诊断结果表明：LightGBM算法较常规机器学习算法能够取得更好的诊断效果，而且在数据集中融入叶片模态参数可明显增加诊断算法的准确率，从而提高叶片裂纹损伤的在线监测准确性。

关键词: 风电机组, 叶片裂纹损伤, 工作模态分析, 传递率, 机器学习

Abstract:

Since crack damage of wind turbine (WT) blades is easy to occur and difficult to find, online monitoring of blade crack damage is carried out by collecting and analyzing blade vibration signals. Firstly, based on the theory of working modal analysis, an online identification method of blade modal parameters based on transmissibility is constructed, and a blade vibration physical experiment platform is built for the experimental verification of the method. By comparing the experimental results with the traditional hammer excitation method, the accuracy of the method is verified. Then, with a 5 MW WT as an example, the blade crack damage fault is simulated, and the damage fault characteristics are obtained through working modal analysis. Finally, blade vibration signals, modal parameters, and WT operation data are fused into multi-source data sets, and blade crack damage fault diagnosis is performed based on the LightGBM algorithm. The diagnosis results show that the LightGBM algorithm can achieve a better diagnosis effect than the conventional machine learning algorithm, and the accuracy of the diagnosis algorithm can be significantly increased by integrating blade modal parameters into the data set, so as to improve the accuracy of online monitoring of blade crack damage.

Key words: wind turbine, blade crack damage, working modal analysis, transmissibility, machine learning

吴宇辉, 张扬帆, 高峰, 王玙, 王耀函, 杨伟新, 张鸿. 基于工作模态分析的风电机组叶片裂纹损伤在线监测研究[J]. 中国电力, 2023, 56(10): 106-114.

Yuhui WU, Yangfan ZHANG, Feng GAO, Yu WANG, Yaohan WANG, Weixin YANG, Hong ZHANG. Research on Online Monitoring of Crack Damage of Wind Turbine Blades Based on Working Modal Analysis[J]. Electric Power, 2023, 56(10): 106-114.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202303035

https://www.electricpower.com.cn/CN/Y2023/V56/I10/106

图/表 14

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模态	传递率法		力锤法
模态	频率/Hz	阻尼比/%	频率/Hz	阻尼比/%
一阶模态	7.69	2.00	7.56	3.76
二阶模态	16.92	1.32	17.71	1.49
三阶模态	33.74	0.79	35.36	0.76
四阶模态	48.38	0.28	48.35	0.30
五阶模态	52.00	0.33	52.43	0.50
六阶模态	67.25	0.29	66.26	0.40

模态	传递率法		力锤法
模态	频率/Hz	阻尼比/%	频率/Hz	阻尼比/%
一阶模态	7.69	2.00	7.56	3.76
二阶模态	16.92	1.32	17.71	1.49
三阶模态	33.74	0.79	35.36	0.76
四阶模态	48.38	0.28	48.35	0.30
五阶模态	52.00	0.33	52.43	0.50
六阶模态	67.25	0.29	66.26	0.40

损伤程度	质量/kg	刚度/(N·m²)	阻尼/%	厚度/m	弦长/m
未损伤	1867.46	摆振：7.229×10⁹ 挥舞：1.022×10¹⁰	0.477465	4.167	4.167
轻微损伤	1867.46	摆振：6.506×10⁹ 挥舞：9.198×10⁹	0.477465	4.167	4.167
严重损伤	1867.46	摆振：4.379×10⁹ 挥舞：6.132×10⁹	0.477465	4.167	4.167

损伤程度	质量/kg	刚度/(N·m²)	阻尼/%	厚度/m	弦长/m
未损伤	1867.46	摆振：7.229×10⁹ 挥舞：1.022×10¹⁰	0.477465	4.167	4.167
轻微损伤	1867.46	摆振：6.506×10⁹ 挥舞：9.198×10⁹	0.477465	4.167	4.167
严重损伤	1867.46	摆振：4.379×10⁹ 挥舞：6.132×10⁹	0.477465	4.167	4.167

是否损伤	功率标准差	转速标准差	加速度标准差	位移标准差
未损伤	1.4782	21.6566	0.2804	0.4503
损伤	1.4805	21.6661	0.2837	0.4816