Characterization and Prediction of Wind Turbine Blade Damage Based on Fiber Grating Sensor

Authors

DOI:

https://doi.org/10.4108/ew.5752

Keywords:

wind turbine, wind turbine blade, damage detection, energy spectrum

Abstract

INTRODUCTION: As a renewable and clean use of energy, wind power generation has a very important role in the new energy generation industry. For the many parts of various wind turbines, the safety and reliability of wind turbine blades are very important.

OBJECTIVES: The energy spectrum simulation algorithm included in the wavelet analysis method is used to simulate and analyzewind turbine blade damage, to verify the correctness and validity of wind turbine blade damage analysis.

METHODS: Matlab simulation is used to introduce the experiments related to the static and dynamic detection of fiber grating sensors, analyze the signal characteristics of the wind turbine blade when it is damaged by the impact, and provide a basis for the analysis of the external damage of large wind turbine blade.

RESULTS: The main results obtained in this paper are the following. By analyzing the decomposition of wavelet packets, the gradient change of wavelet impact energy spectrum before and after the wavelet damage was obtained and compared with the histogram, and the impact energy spectrum of each three-dimensional wavelet energy packet in the image was compared and analyzed, which can well realize the recognition of wavelet damage gradient for solid composite materials.

CONCLUSION: With the help of Matlab simulation to collect the impact response signal, using the wavelet packet energy spectrum method to analyze the signal, can derive the characteristics of wind turbine blade damage.

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References

Li, X. Few-shot wind turbine blade damage early warning system based on sound signal fusion. Multimedia Systems 29, 2913–2922 (2023). DOI: https://doi.org/10.1007/s00530-021-00882-7

Rani Manjeet et al. Development of sustainable microwave-based approach to recover glass fibers for wind turbine blades composite waste. Resources, Conservation & Recycling, 2022, 179. DOI: https://doi.org/10.1016/j.resconrec.2021.106107

Dietmar, Tilch, Daniel, et al. Condition Monitoring of Rotor Blades:Damages, ICe, Overload //The 13th World Wind Energy Conference(WWEC2014). 2014:1-8,52.

Jihong Guo. Research on damage detection of fan blades based on computer vision and deep learning algorithm. Qingdao University of technology, 2021 DOI:10.27263/d.cnki. gqudc. 2021.000144.

Jian Dong. Research on fault early warning and life assessment method and its application of key components of wind turbine. North China Electric Power University (Beijing), 2021 DOI:10.27140/d.cnki. ghbbu. 2021.000126.

Gongtian Shen. Development status of non-destructive testing and evaluation technology for pressure equipment. Journal of mechanical engineering, 2017, 53 (12):1-12. DOI: https://doi.org/10.3901/JME.2017.12.001

LIN Feng, GUO Peng, LIU Xubin. Wind Turbine Blade Surface Damage Identification Based on Blade Surface Dirt Pre-treatment and CNN. Journal of Chinese Society of Power Engineering. 2020, 40(12):975-981.

Yu Y, Yong Z, Jian W, et al. Research on Wind Turbine Blade Damage Identification Method Based on Thermal Infrared Image. Acta Energiae Solaris Sinica, 2022, 43(2):492.

Jia Hui, Zhang Leian, Wang Jinghua, et al. Damage pattern recognition of wind turbine blade composite material based on acoustic emission technology. Renewable Energy Resources, 2022, 40(01):67-72. DOI:10.13941/j.cnki.21-1469/tk.2022.01.008.

YU Yang, LI Yun, YANG Ping, et al. Improved wavelet threshold function and ACEWT method for feature extraction of acoustic emission signals from rolling bearing faults. Journal of Vibration and Shock. 2023, 42(17):194-202.DOI:10.13465/j.cnki.jvs.2023.17.025.

Changing Yang, Luping Li. Research progress of wind turbine blade damage fault detection technology. Power generation technology, 2020,41 (06): 599-607.

Solimine J, Inalpolat M. An unsupervised data-driven approach for wind turbine blade damage detection under passive acoustics-based excitation. Wind Engineering. 2022;46(4):1311-1330. DOI: https://doi.org/10.1177/0309524X221080470

Pingyu Zhu et al. Reliable packaging of optical fiber Bragg grating sensors for carbon fiber composite wind turbine blades. Composites Science and Technology, 2021, : 108933-. DOI: https://doi.org/10.1016/j.compscitech.2021.108933

Xiaohong Bai. Design, fabrication and performance optimization of fiber Bragg grating ultrasonic sensor. Northwestern University, 2021 DOI:10.27405/d.cnki. gxbdu. 2021.002187.

Zhaohui Zhang Wind turbine structure state evaluation method based on optical fiber sensing technology. Harbin Institute of technology, 2020 DOI:10.27061/d.cnki. ghgdu. 2020.004670.

Lihua Yu, Changsheng Chen, Shulong Liu, et al. Using MATLAB software to simulate vibration experiment. College physics experiment, 2011,24 (3):79-81 DOI:10.3969/j.issn. 1007-2934.2011.03.024.

Sayed Ahmed Zaki Ahmed. Photovoltaic system fault detection and diagnosis method based on intelligent algorithm. North China Electric Power University (Beijing), 2021 DOI:10.27140/d.cnki. ghbbu. 2021.000136.

Muhammad Abubakar. Effective detection and classification of multiple power quality disturbances based on statistical parameters and deep learning. Jiangsu University, 2020 DOI:10.27170/d.cnki. gjsuu. 2020.000526.

Contents and Abstracts of Journal of Mechanical Engineering. Chinese Journal of Mechanical Engineering, 2010, 23(01):135-158. Vol.46, No.1~4, 2010. DOI: https://doi.org/10.3901/CJME.2010.01.001

Contents and Abstracts of Journal of Mechanical Engineering. Chinese Journal of Mechanical Engineering, 2009, 22(05):772-790. ISSN 0577-6686,CN 11-2187/TH.

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Published

12-04-2024

How to Cite

1.
Guan X, Mu Q, Yin X, Wang Y. Characterization and Prediction of Wind Turbine Blade Damage Based on Fiber Grating Sensor. EAI Endorsed Trans Energy Web [Internet]. 2024 Apr. 12 [cited 2024 Dec. 22];11. Available from: https://publications.eai.eu/index.php/ew/article/view/5752

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