π-FBG Fiber Optic Acoustic Emission Sensor for the Crack Detection of Wind Turbine Blades

Author:

Yan Qi1,Che Xingchen1,Li Shen1,Wang Gensheng2,Liu Xiaoying13ORCID

Affiliation:

1. School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China

2. SPIC Jiangxi Electric Power Co., Ltd., Nanchang 330096, China

3. Technology Research Institute, Shenzhen Huazhong University of Science, Shenzhen 518057, China

Abstract

Wind power is growing rapidly as a green and clean energy source. As the core part of a wind turbine, the blades are subjected to enormous stress in harsh environments over a long period of time and are therefore extremely susceptible to damage, while at the same time, they are costly, so it is important to monitor their damage in a timely manner. This paper is based on the detection of blade damage using acoustic emission signals, which can detect early minor damage and internal damage to the blades. Instead of conventional piezoelectric sensors, we use fiber optic gratings as sensing units, which have the advantage of small size and corrosion resistance. Furthermore, the sensitivity of the system is doubled by replacing the conventional FBG (fiber Bragg grating) with a π-phase-shifted FBG. For the noise problem existing in the system, this paper combines the traditional WPD (wavelet packet decomposition) denoising method with EMD (empirical mode decomposition) to achieve a better noise reduction effect. Finally, small wind turbine blades are used in the experiment and their acoustic emission signals with different damage are collected for feature analysis, which sets the stage for the subsequent detection of different damage degrees and types.

Funder

Shenzhen Science and Technology Programe

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference22 articles.

1. Global Wind Energy Council (2021). GWEC|Global Wind Report 2021, Global Wind Energy Council.

2. Survey of failures in wind power systems with focus on Swedish wind power plants during 1997–2005;Ribrant;IEEE Trans. Energy Convers,2007

3. A data-driven design for fault detection ofwind turbines using random forests and XGBoost;Zhang;IEEE Access,2018

4. Condition monitoring of wind turbines: Techniques and methods;Tobias;Renew. Energy,2012

5. Study of fatigue damage in wind turbine blades;Barroso;Eng. Fail. Anal.,2009

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