The Third-Order Viscoelastic Acoustic Model Enables an Ice-Detection System for a Smart Deicing of Wind-Turbine Blade Shells

Noise generated by turbulent boundary layer over the trailing edge of a wind turbine blade under various flow conditions is predicted and analyzed for structural health monitoring purposes. Wind turbine blade monitoring presents a challenge to wind farm operators, and an in-blade structural health monitoring system would significantly reduce O&M costs. Previous studies into structural health monitoring of blades have demonstrated the feasibility of designing a passive detection system based on monitoring the flow-generated acoustic spectra. A beneficial next step is identifying the robustness of such a system to wind turbine blades under different flow conditions. To examine this, a range of free stream air velocities from 5 m/s to 20 m/s and a range of rotor speeds from 5 rpm to 20 rpm are used in a reduced-order model of the flow-generated sound in the trailing edge turbulent boundary layer. The equivalent lumped acoustics sources are predicted based on the turbulent flow simulations, and acoustic spectra are calculated using acoustic ray tracing. Each case is evaluated based on the changes detected when damage is present. These results can be used to identify wind farms that would most benefit from this monitoring system to increase efficiency in deployment of turbines.

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Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis

Renewable Energy ◽

10.1016/j.renene.2018.08.050 ◽

2019 ◽

Vol 132 ◽

pp. 1034-1048 ◽

Cited By ~ 31

Author(s):

Alfredo Arcos Jiménez ◽

Fausto Pedro García Márquez ◽

Victoria Borja Moraleda ◽

Carlos Quiterio Gómez Muñoz

Keyword(s):

Machine Learning ◽

Wind Turbine ◽

Turbine Blade ◽

Wind Turbine Blade ◽

Nonlinear Features ◽

Linear And Nonlinear ◽

Detection And Diagnosis ◽

Ice Detection

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A Wind Turbine Blade Damage Detection System Based on Data Analysis: An Academic Example on the Use of Histograms

Renewable Energy and Power Quality Journal ◽

10.24084/repqj16.365 ◽

2018 ◽

Vol 1 ◽

pp. 505-509

Author(s):

Leonardo Acho ◽

◽

Pablo Buenestado

Keyword(s):

Data Analysis ◽

Damage Detection ◽

Wind Turbine ◽

Turbine Blade ◽

Detection System ◽

Wind Turbine Blade

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Study on wind turbine blade defect detection system based on imaging array

E3S Web of Conferences ◽

10.1051/e3sconf/201911802041 ◽

2019 ◽

Vol 118 ◽

pp. 02041 ◽

Cited By ~ 1

Author(s):

Ningning Zhang ◽

Chengzhi Lu ◽

Anmin Wang

Keyword(s):

Wind Turbine ◽

Turbine Blade ◽

Defect Detection ◽

Wind Farm ◽

Detection Efficiency ◽

Detection System ◽

Turbine Blades ◽

Wind Turbine Blade ◽

Quality Analysis ◽

Defect Recognition

Currently in the process of wind farm inspection, wind turbine blade appearance inspection mainly adopts the telescope or high-definition cameras, low detection efficiency, labor intensity and the precision is limited, in order to solve this problem, a kind of wind turbine blades defect recognition system based on image array is proposed. Through the joint of array camera and image processing server, the functions of the image acquisition, processing, and defect recognition and detection results output are implemented. The software of artificial intelligence deep learning based on neural network algorithm is used to identify the defects of blade image, and output quality analysis report, to realize automatic detection of wind turbine blade surface defect. The field measurement results show that the system greatly improves the efficiency and accuracy of wind turbine blade defect detection.

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