Wind turbine blade trailing edge crack detection based on airfoil aerodynamic noise: An experimental study

Zhang, Yanan; Avallone, Francesco; Watson, Simon J.

doi:10.1016/j.apacoust.2022.108668

“…However, the peaks are not very significant but present as broadband humps. This is because, for a blunter trailing edge, more coherent vortex structures are shed [68,69]. Comparing the results of our experiment to N. D. Kelly [68], the investigation of acoustic noise associated with the MOD-1 turbine showed that the region where the two humps were located on the SPL vs frequency plots were not associated with the rotational aspect of the wind turbine noise because rotational noise, as expected, would be observed at lower frequencies.…”

Section: Discussionsupporting

confidence: 62%

“…Y. Zhang [69] investigated wind turbine blade trailing crack detection based on airfoil aerodynamic noise in a wind tunnel. In Figure 5.4, the baseline of the airfoil when zoomed in, is seen to have peaks similar to what was observed in the present work.…”

Section: Discussionmentioning

confidence: 99%

Wind Tunnel Testing to Evaluate Noise Emissions from a Small Wind Turbine

Asi¹

0

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The wind power industry has experienced fast and tremendous growth, globally, in recent years. Consequently, there is a growing interest in investigating aerodynamics and aeroacoustics characteristics of wind turbines and developing environmental standards aimed at decreasing noise levels in the design and manufacture of these machines.This study was carried out in the Carleton University Wind Induced Dynamic Laboratory (WInDLab) that was designed to evaluate and quantify noise levels emitted by a small horizontal axis wind turbine. This investigation provided the firstever set of measurements carried out in the WInDLab following the successful installation of the wind turbine.To achieve the objective of this project, first, the characterization of the wind tunnel had to be carried out. The wind velocity profile of the wind tunnel was characterized and results indicated that its profile is not perfectly symmetrical with respect to the tunnel centreline.The background noise levels of the WInDLab wind tunnel for different fan RPMs were recorded with a Brüel & Kjaer (B&K) Prepolarized Free-field Type 4189, ½"(1.27 cm) microphone, which was located at six positions in line with IEC61400-11 regulations. The noise measurements obtained were post-processed using the MATLAB software application. Plots for the sound pressure level (SPL) vs frequency of the wind turbine noise spectra, as well as the background noise level at 500 RPM, 575 RPM and 650 RPM of the wind tunnel fans were derived. I would like to express my sincere gratitude to my supervisors, Prof. Fred Afagh, Prof. Robert Langlois, and Prof. Joana Rocha for their advice, guidance, and financial support, in the course the project. A major part of my project work was the characterization of the wind tunnel and the acoustic noise measurements. I thank Mr. James Cann, Mr. Chris Bassindale, and Mr. David Raude, expert technologists in Carleton University's Mechanical and Aerospace Engineering Department who provided the support and guidance in the installation of the wind turbine, characterization of the wind tunnel, and the acoustic noise measurements of the wind tunnel. I thank Mr. Alex Proctor who assisted with the machining and assembling of the wind turbine tower. I am deeply grateful to my family for their unwavering support throughout the duration of this project. I thank my dad, my mom, my sisters and my brother for their prayers, encouragement, emotional support, financial support, and advice during my Master's degree program. I cannot forget Saeid Naeini for his help and ideas during the project. Lastly, I would like to thank all my friends who have supported me in one way or the other through the course of my project. v

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“…These investigations suggested that the cause of the humps is possibly the result of trailing edge separation and vortex shedding. Y. Zhang [14] investigated wind turbine blade trailing crack detection based on airfoil aerodynamic noise in a wind tunnel. Figure 9 shows the SPL vs Frequency plot for the baseline and various crack widths, W of the airfoil.…”

Section: Discussionmentioning

confidence: 99%

Wind tunnel testing to evaluate noise emissions from a small wind turbine

Asi,

Afagh,

Langlois

et al. 2023

Progress in Canadian Mechanical Engineering. Volume 6

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The wind power industry has experienced fast and tremendous growth, globally, in recent years. Consequently, there is a growing interest in investigating aerodynamics and aeroacoustics characteristics of wind turbines and developing environmental standards aimed at decreasing noise levels in the design and manufacture of these machines. A wind tunnel test was conducted to characterize the noise level emitted by a Sunforce small 400 W wind turbine at average wind speeds exceeding 4.0 -4.5 m/s, which is required for a small wind turbine to operate at maximum power output levels [1]. This study was carried out in the Carleton University Wind Induced Dynamics Laboratory (WInDLab) in order to evaluate and quantify noise levels emitted by a small horizontal axis wind turbine. This investigation provided the first-ever set of measurements carried out in the WInDLab following the successful installation of the wind turbine. To achieve the objective of this project, first, the characterization of the wind tunnel had to be carried out.

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“…The structural health monitoring methods of WTBs in the literature can be categorized into two classes: non-contact and contact measurement [8,10]. For instance, Wang et al [11] used unmanned aerial vehicles (UAVs) to collect WTB images, accomplishing WTB defect recognition and location through image noise reduction processing and Haar-like feature extraction, and used a cascade classifier to analyze the features.…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Blade Defect Detection Based on Acoustic Features and Small Sample Size

Zhu

¹

,

Liu

²

,

Shen

³

et al. 2022

Machines

5

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Wind power has become an important source of electricity for both production and domestic use. However, because wind turbines often operate in harsh environments, they are prone to cracks, blisters, and corrosion of the blade surface. If these defects cannot be repaired in time, the cracks evolve into larger fractures, which can lead to blade rupture. As such, in this study, we developed a remote non-contact online health monitoring and warning system for wind turbine blades based on acoustic features and artificial neural networks. Collecting a large number of wind turbine blade defect signals was challenging. To address this issue, we designed an acoustic detection method based on a small sample size. We employed the octave to extract defect information, and we used an artificial neural network based on model-agnostic meta-learning (MAML-ANN) for classification. We analyzed the influence of locations and compared the performance of MAML-ANN with that of traditional ANN. The experimental results showed that the accuracy of our method reached 94.1% when each class contained only 50 data; traditional ANN achieved an accuracy of only 85%. With MAML-ANN, the training is fast and the global optimal solution is automatic searched, and it can be expanded to situations with a large sample size.

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Wind turbine blade trailing edge crack detection based on airfoil aerodynamic noise: An experimental study

Cited by 14 publications

References 64 publications

Wind Tunnel Testing to Evaluate Noise Emissions from a Small Wind Turbine

Wind Tunnel Testing to Evaluate Noise Emissions from a Small Wind Turbine

Wind tunnel testing to evaluate noise emissions from a small wind turbine

Wind Turbine Blade Defect Detection Based on Acoustic Features and Small Sample Size

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