基于模态频率的风力机叶片覆冰检测方法

复合材料科学与工程 ›› 2021, Vol. 0 ›› Issue (2): 19-23.

基于模态频率的风力机叶片覆冰检测方法

李飞宇, 崔红梅^*, 苏宏杰, 王念富, 马志鹏

内蒙古农业大学机电工程学院, 呼和浩特010018

收稿日期:2020-06-11 出版日期:2021-02-28 发布日期:2021-03-10
通讯作者: 崔红梅(1978-),女,副教授,硕士生导师,主要研究方向为工程测试与控制,chm123m@126.com。
作者简介:李飞宇(1990-),男,硕士,工程师,主要从事风力发电机叶片模态测试技术方面的研究。
基金资助:
国家自然科学基金项目(11262015);内蒙古农业大学优秀青年科学基金项目(2014XYQ-9)

DETECTION OF ICE ACCRETION ON WIND TURINE BLADE ON MODAL FREQUENCY

LI Fei-yu, CUI Hong-mei^*, SU Hong-jie, WANG Nian-fu, MA Zhi-peng

The College of Electromechanical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China

Received:2020-06-11 Online:2021-02-28 Published:2021-03-10

摘要/Abstract

摘要： 目前,风力机叶片覆冰后通常使用加热系统除冰,其消耗的能源是发电机组1%~4%的年发电量。本文通过试验模态调校叶片有限元模型,用有限元模型的仿真模态得出不同位置的覆冰厚度和固有频率对应关系方程,用这些方程生成随机样本训练BP神经网络模型,建立以固有频率为输入,以覆冰厚度为输出的非线性关系,以实现覆冰状态的检测。研究表明,通过叶片的力锤激励模态试验结果,调整叶片模型参数,优化后的叶片覆冰中空实体三维模型前三阶固有频率与试验值误差在2%以内。通过BP神经网络建模和训练,模型检测覆冰厚度的结果与实际值相对误差率平均值为8.83%,叶尖处误差最小,叶根处误差最大,相对误差率随着冰层厚度的增加而降低。训练好的BP神经网络模型可以基本实现覆冰的位置和厚度信息检测,为加热系统精确加热位置和加热时间、降低能源消耗提供理论依据。

关键词: 风力机叶片, 覆冰检测, 模态参数, 固有频率, 复合材料

Abstract: At present, the wind turbine blades are usually de-iced using a heating system after ice coating, and the energy consumption is about 1%~4% of the annual power generation of the generator set. In this paper, the finite element model is adjusted by experimental modal results, and the equations of the corresponding relationship between the thickness of the ice coating and the natural frequency at different positions are obtained by the simulation modes of the finite element model. These equations are used to generate random samples to train the BP neural network model. The frequency is the input, and the thickness of the ice is the nonlinear relationship of the output to realize the detection of the ice status. The research shows that through the results of the modal test of the blade′s force hammer excitation, the parameters of the blade model are adjusted, and the error between the first three-order natural frequency and the test value of the optimized three-dimensional model of the bladed iced hollow solid is within 2%. Through BP neural network modeling and training, the average error rate of the results of the model detection ice thickness and the actual value is 8.83%, the error at the blade tip is the smallest, and the error at the blade root is the largest, the relative error rate decreased with the increase of the ice thickness. The trained BP neural network model can basically realize the detection of ice coating position and thickness information, and provide a theoretical basis for the precise heating position and heating time of the heating system and reducing energy consumption.

Key words: wind turbine blade, icing detection, modal parameters, natural frequency, composites

中图分类号:

TB332

李飞宇, 崔红梅, 苏宏杰, 王念富, 马志鹏. 基于模态频率的风力机叶片覆冰检测方法[J]. 复合材料科学与工程, 2021, 0(2): 19-23.

LI Fei-yu, CUI Hong-mei, SU Hong-jie, WANG Nian-fu, MA Zhi-peng. DETECTION OF ICE ACCRETION ON WIND TURINE BLADE ON MODAL FREQUENCY[J]. COMPOSITES SCIENCE AND ENGINEERING, 2021, 0(2): 19-23.

参考文献

[1] 国家能源局. 2019年风电并网运行情况[EB/OL]. (2019-02-28)[2020-06-02]. http://www.nea.gov.cn/2020-02/28/c_138827910.htm.
[2] 李姜超, 成斌, 李西洋. 基于响应曲面法的风力机叶片覆冰载荷影响因素分析[J]. 玻璃钢/复合材料, 2018(9): 17-22.
[3] SKRIMPAS G A, KLEANI K, MIJATOVIC N, et al. Detection of icing on wind turbine blades by means of vibration and power curve analysis[J]. Wind Energy, 2015, 19(10): 1819-1832.
[4] GANTASALA S, LUNENO J C, AIDANPÄÄ J O. Influence of icing on the modal behavior of wind turbine blades[J]. Energies , 2016, 9(11): 862-868.
[5] 刘胜先, 李录平, 余涛, 等. 基于振动检测的风力机叶片覆冰状态诊断技术[J]. 中国电机工程学报, 2013, 33(32): 88-95.
[6] 雷利斌, 李录平, 刘胜先, 等. 基于振型曲率的风力机叶片覆冰检测技术[J]. 太阳能学报, 2014, 35(5): 841-847.
[7] PELTOLA E, LAAKSO T, ANTIKAINEN P, et al. Prevention of icing effects[C]//Finland: Proceedings of BOREAS VI conference. 2003: 9-11.
[8] 陈红星. 薄覆冰状态下小型风力发电机叶片模态试验研究[D]. 内蒙古: 内蒙古农业大学, 2019, 12-15.
[9] 沃德·海伦, 斯蒂芬·拉门兹, 波尔·萨斯. 模态分析理论与试验[M]. 白化同, 郭继忠, 译. 北京: 北京理工大学出版社, 2001: 8-57.
[10] LLOYD G. Guideline for the certification of wind turbines[S]. Gemanischer Lioyd Wind Energie Gm BH, 2010.
[11] 单丽君, 王萌. 风力机叶片优化设计及模态分析研究[J]. 机械设计, 2014, 31(2): 64-68.
[12] 毛火军, 石可重, 李宏利, 等.大型风电叶片的模态测试与数值模拟[J]. 工程热物理学报, 2009, 30(4): 601-604.
[13] SCHUTZ J B. Properties of composite materials for cryogenic applications[J]. Cryogenics, 1998, 38: 3-12.
[14] GANTASALA S, LUNENO J C, AIDANPÄÄ J O. Identification of ice mass accumulated on wind turbine blades using its natural frequencies[J]. Wind Engineering, 2018, 42(1): 66-84.
[15] 韩敏. 人工神经网络基础[M]. 大连: 大连理工大学出版社, 2001: 21-38.
[16] MEHROTRA K, MOHAN C, RANKA S. Elements of artificial neural networks[M]. MA: MIT Press, 1996: 35-88.
[17] 龚妙, 李录平, 刘瑞, 等. 基于运行参数特征的风力机叶片覆冰诊断方法[J]. 动力工程学报, 2019, 39(3): 214-219.