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

Author:

Guan Xin,Mu Qizheng,Yin Xiaoju,Wang Yuxin

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.

Publisher

European Alliance for Innovation n.o.

Reference20 articles.

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

4. 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.

5. 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.

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