RESEARCH ON SURFACE CRACK IDENTIFICATION METHOD OF LARGE WIND TURBINE BLADE BASED ON WPT-SVD-KELM

Liu Qindong; Luo Yongshui; Zhang Junhua; Ai Zhenwei; Cheng Qichao; Yang Shixi

doi:10.19912/j.0254-0096.tynxb.2021-1258

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (3) : 155-161. DOI: 10.19912/j.0254-0096.tynxb.2021-1258

RESEARCH ON SURFACE CRACK IDENTIFICATION METHOD OF LARGE WIND TURBINE BLADE BASED ON WPT-SVD-KELM

Liu Qindong^1,2, Luo Yongshui^1~3, Zhang Junhua^1,2, Ai Zhenwei^1,2, Cheng Qichao³, Yang Shixi³

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Abstract

In order to accurately identify the location and length of surface cracks on wind turbine blades, a crack identification method based on wavelet packet decomposition - singular value decomposition - nuclear limit learning machine (WPT-SVD-KELM) is proposed. The crack identification experiment platform of a typical blade was built, modal experiments and pitch experiments of normal blades and cracked blades were carried out, and vibration signals of normal blades and cracked blades under different working conditions were obtained. The frequency response function is used to study the influence of the crack position on the amplitude-frequency response of the vibration signal, so as to accurately identify the position of the crack on the blade surface. The WPT-SVD is used to extract the time-frequency characteristics of the wind turbine blade surface crack vibration signal, and the parameter k_r is defined to characterize the change of the crack length. The characteristic parameters are imported into the optimized KELM to identify the length of surface crack on the wind turbine blade. The research results have important guiding significance for the identification of surface cracks on large wind turbine blades.

Key words

wind turbine blades / crack detection / wavelet decomposition / singular value decomposition / kernel extreme learning machine / abnormal recognition

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Liu Qindong, Luo Yongshui, Zhang Junhua, Ai Zhenwei, Cheng Qichao, Yang Shixi. RESEARCH ON SURFACE CRACK IDENTIFICATION METHOD OF LARGE WIND TURBINE BLADE BASED ON WPT-SVD-KELM[J]. Acta Energiae Solaris Sinica. 2023, 44(3): 155-161 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1258

References

[1] ZHOU H F, DOU H Y, QIN L Z, et al.A review of full-scale structural testing of wind turbine blades[J]. Renewable sustainable energy review, 2014, 33: 177-187.
[2] CAITHNESS W F.Summary of wind turbine accident data to 30 September 2020[R]. 2020.
[3] ALIAN H, KONFORTY S, BEN-SIMON U, et al.Bearing fault detection and fault size estimation using fiber-optic sensors[J]. Mechanical systems and signal processing, 2019, 120: 392-407.
[4] OLIVEIRA G, MAGALHAES F, CUNHA A, et al.Vibration-based damage detection in a wind turbine using 1year of data[J]. Structural control and health monitoring, 2018, 25(11): e2238.
[5] TSOPELAS N K, PAPASALOUROS D G, ANASTASOPOULOS A A, et al.Acoustic emission for structural integrity assessment of wind turbine blades[J]. Advances in acoustic emission technology, 2015, 1: 369-382.
[6] ZUO H, YANG Z B, XU C B, et al.Damage identification for plate-like structures using ultrasonic guided wave based on improved MUSIC method[J]. Composite structures, 2018, 203: 164-171.
[7] HWANG S, AN Y K, SOHN H, et al.Continuous-wave line laser thermography for monitoring of rotating wind turbine blades[J]. Structural health monitoring, 2019, 18: 1010-1021.
[8] WANG L, ZHANG Z J.Automatic detection of wind turbine blade surface cracks based on UAV-taken images[J]. IEEE transactions on industrial electronics, 2017, 64(9): 7293-7303.
[9] 吴琪强, 郭帅平, 王钢, 等. 基于风力机叶片固有频率的裂纹定位方法[J]. 机械强度, 2019, 41(5): 1048-1053.
WU Q Q, GUO S P, WANG G, et al.Crack indirect identification method for dynamic vibration frequency of wind blades[J]. Journal of mechanical strength, 2019, 41(5): 1048-1053.
[10] 耿晓锋, 魏克湘, 王琼, 等. 基于多频简谐调制的风力机叶片裂纹检测研究[J]. 振动与冲击, 2018, 37(22): 201-205.
GENG X F, WEI K X, WANG Q, et al.Crack detection method for wind turbine blades based on the method of multi-frequency harmonic modulation[J]. Journal of vibration and shock, 2018, 37(22): 201-205.
[11] 刘晓波, 林家国, 王依. 基于小波包分析的风力机叶片裂纹故障识别研究[J]. 机床与液压, 2007(9): 241-243.
LIU X B, LIN J G, WANG Y.Research on fault identification of blade crack of fan based on wavelet-packet analysis[J]. Machine tool & hydraulics, 2007(9): 241-243.
[12] 李小伟. 基于光纤光栅传感技术的风力机叶片检测研究[D]. 南京: 南京航空航天大学, 2013.
LI X W.Research on wind turbine blades detection based on fiber bragg grating sensing technology[D]. Nanjing: Transactions of Nanjing University of Aeronautics & Astronautics, 2013.
[13] 邹洁, 何超, 朱永凯. 基于FBG和小波包能量谱的风力机叶片无损检测[J]. 电子测量技术, 2015, 38(3): 133-138.
ZOU J, HE C, ZHU Y K.Research on the NDT of fan blade based on fiber bragg grating and wavelet packet energy spectrum technology[J]. Electronic measurement technology, 2015, 38(3): 133-138.
[14] HUANG G B, ZHOU H, DING X, et al.Extreme learning machine for regression and multiclass classification[J]. IEEE transactions on systems, man and cybernetics, Part B (Cybernetics), 2012, 42(2): 513-529.