RESEARCH ON FAULT SIMULATION AND STATE JUDGMENT OF WIND TURBINE BLADES

Gao Feng; Zhang Hong; Xu Lin; Liu Juncheng

doi:10.19912/j.0254-0096.tynxb.2021-1434

PDF(1704 KB)

Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (4) : 52-59. DOI: 10.19912/j.0254-0096.tynxb.2021-1434

RESEARCH ON FAULT SIMULATION AND STATE JUDGMENT OF WIND TURBINE BLADES

Gao Feng, Zhang Hong, Xu Lin, Liu Juncheng

Author information +

History +

Abstract

Blade icing and damage faults were simulated by the software Bladed in this paper. By comparing and analyzing the operation data of wind turbine under blades normal and fault conditions, the change characteristics of operation data under the condition of blade failure was found. Then, the blade vibration signals were used to identify the blade modal parameters based on the blade operational modal analysis theory. According to the variation of blade modal parameters, the fault difference between blade icing and damage on blade vibration was revealed. Finally, the identified blade modal parameters and wind turbine operation parameters are combined into multi-source data. The classification decision tree algorithm under the framework of LightGBM （Light Gradient Boosting Machine） is used to identify the blade fault state effectively.

Key words

wind turbine blade / damage detection / failure analysis / learning algorithms / icing / LightGBM

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Gao Feng, Zhang Hong, Xu Lin, Liu Juncheng. RESEARCH ON FAULT SIMULATION AND STATE JUDGMENT OF WIND TURBINE BLADES[J]. Acta Energiae Solaris Sinica. 2023, 44(4): 52-59 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1434

References

[1] YANG B, SUN D B.Testing, inspecting and monitoring technologies for wind turbine blades: a survey[J]. Renewable and sustainable energy reviews, 2013, 22: 515-526.
[2] DU Y, ZHOU S X, JING X J, et al.Damage detection techniques for wind turbine blades: a review[J]. Mechanical systems and signal processing, 2020, 141: 106445.
[3] 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.
GONG M, LI L P, LIU R, et al.Diagnosis of ice accretion on wind turbine blades based on the features of operating parameters[J]. Journal of Chinese Society of Power Engineering, 2019, 39(3): 214-219.
[4] YUN H G, ZHANG C K, HOU C G, et al.An adaptive approach for ice detection in wind turbine with inductive transfer learning[J]. IEEE access, 2019, 7: 122205-122213.
[5] CHEN L T, XU G H, ZHANG Q, et al.Learning deep representation of imbalanced SCADA data for fault detection of wind turbines[J]. Measurement, 2019, 139: 370-379.
[6] SALEH S A, AHSHAN R, MOLONEY C R.Wavelet-based signal processing method for detecting ice accretion on wind turbines[J]. IEEE transactions on sustainable energy, 2012, 3(3): 585-597.
[7] MUÑOZ C Q G, MÁRQUEZ F P G, TOMÁS G M S. Ice detection using thermal infrared radiometry on wind turbine blades[J]. Measurement, 2016, 93: 157-163.
[8] RIZK P, SALEH N A, YOUNES R, et al.Hyperspectral imaging applied for the detection of wind turbine blade damage and icing[J]. Remote sensing applications: society and environment, 2020, 18: 100291.
[9] REZAMAND M, KORDESTANI M, CARRIVEAU R, et al.A new hybrid fault detection method for wind turbine blades using recursive PCA and wavelet-based PDF[J]. IEEE sensors journal, 2020, 20(4): 2023-2033.
[10] WANG L, ZHANG Z J, XU J, et al.Wind turbine blade breakage monitoring with deep auto encoders[J]. IEEE transactions on smart grid, 2018, 9(4): 2824-2833.
[11] HWANG S, AN Y K, SOHN H.Continuous line laser thermography for damage imaging of rotating wind turbine blades[J]. Procedia engineering, 2017, 188: 225-232.
[12] YU Y J, CAO H, YAN X Y, et al.Defect identification of wind turbine blades based on defect semantic features with transfer feature extractor[J]. Neurocomputing, 2020, 376: 1-9.
[13] TANG J L, SOUA S, MARES C, et al.An experimental study of acoustic emission methodology for inservice condition monitoring of wind turbine blades[J]. Renewable energy, 2016, 99: 170-179.
[14] XU D, LIU P F, CHEN Z P, et al.Achieving robust damage mode identification of adhesive composite joints for wind turbine blade using acoustic emission and machine learning[J]. Composite structure, 2020, 236: 111840.
[15] OH K Y, PARK J Y, LEE J S, et al.A novel method and its field tests for monitoring and diagnosing blade health for wind turbines[J]. IEEE transactions on instrumentation and measurement, 2015, 64(6): 1726-1733.
[16] SIERRA-PÉREZ J, TORRES-ARREDONDO M A, GÜEMES A. Damage and nonlinearities detection in wind turbine blades based on strain field pattern recognition. FBGs, OBR and strain gauges comparison[J]. Composite structures, 2016, 135: 156-166.
[17] DOWNEY A, UBERTINI F, LAFLAMME S.Algorithm for damage detection in wind turbine blades using a hybrid dense sensor network with feature level data fusion[J]. Journal of wind engineering and industrial aerodynamics, 2017, 168: 288-296.
[18] GONZÁLEZ A G, FASSOIS S D. A supervised vibration-based statistical methodology for damage detection under varying environmental conditions & its laboratory assessment with a scale wind turbine blade[J]. Journal of sound and vibration, 2016, 366: 484-500.
[19] JIANG S C, LIN P, CHEN Y M, et al.Mixed-signal extraction and recognition of wind turbine blade multiple-area damage based on improved Fast-ICA[J]. Optics, 2019, 179: 1152-1159.
[20] TCHERNIAK D, MOLGAARD L L.Active vibration-based structural health monitoring system for wind turbine blade: demonstration on an operating Vestas V27 WT[J]. Structural health monitoring, 2017, 16(5): 536-550.
[21] GARCÍA D, TCHERNIAK D. An experimental study on the data-driven structural health monitoring of large wind turbine blades using a single accelerometer and actuator[J]. Mechanical systems and signal processing, 2019, 127: 102-119.