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

2019 ◽  
Vol 118 ◽  
pp. 02041 ◽  
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.

Research on Wind Turbine Blade Surface Damage Fault On-Line Monitoring and Diagnosis System

2013 ◽  
Author(s):  
Yongxin Feng ◽  
Tao Yang ◽  
Xiaowen Deng ◽  
Qingshui Gao ◽  
Chu Zhang ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Farm ◽  
Turbine Blades ◽  
Surface Damage ◽  
Wind Turbine Blade ◽  
Offshore Wind ◽  
Blade Surface ◽  
Diagnosis System ◽  
On Line

The basic fault types of wind turbine blades are introduced, a novel blade surface damage detection method based on machine vision is being suggested. The network of wind turbine blade surface damage fault on-line monitoring and fault diagnosis system has already been developed. The system architecture, software modules and functions are described, and given application example illustrates the usefulness and effectiveness of this system. The result shows that this system can monitor the surface damage failure of the blade in real time, and can effectively reduce the blade’s maintenance costs for wind farms, especially offshore wind farm.

scholarly journals Defect Detection on a Wind Turbine Blade Based on Digital Image Processing

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1452
Author(s):  
Liwei Deng ◽  
Yangang Guo ◽  
Borong Chai
Keyword(s):  
Power Generation ◽  
Image Processing ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Defect Detection ◽  
Gabor Filter ◽  
Turbine Blades ◽  
Wind Turbine Blade ◽  
Wind Turbine Blades ◽  
Log Gabor

Wind power generation is a widely used power generation technology. Among these, the wind turbine blade is an important part of a wind turbine. If the wind turbine blade is damaged, it will cause serious consequences. The traditional methods of defect detection for wind turbine blades are mainly manual detection and acoustic nondestructive detection, which are unsafe and time-consuming, and have low accuracy. In order to detect the defects on wind turbine blades more safely, conveniently, and accurately, this paper studied a defect detection method for wind turbine blades based on digital image processing. Because the log-Gabor filter used needed to extract features through multiple filter templates, the number of output images was large. Firstly, this paper used the Lévy flight strategy to improve the PSO algorithm to create the LPSO algorithm. The improved LPSO algorithm could successfully solve the PSO algorithm’s problem of falling into the local optimal solution. Then, the LPSO algorithm and log-Gabor filter were used to generate an adaptive filter, which could directly output the optimal results in multiple feature extraction images. Finally, a classifier based on HOG + SVM was used to identify and classify the defect types. The method extracted and identified the scratch-type, crack-type, sand-hole-type, and spot-type defects, and the recognition rate was more than 92%.

scholarly journals Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification

2021 ◽  
Vol 7 (3) ◽  
pp. 46
Author(s):  
Jiajun Zhang ◽  
Georgina Cosma ◽  
Jason Watkins
Keyword(s):  
Deep Learning ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Defect Detection ◽  
Recognition Rate ◽  
Weighted Average ◽  
Wind Turbine Blade ◽  
Evaluation Measures ◽  
Augmentation Techniques ◽  
The Mean

Demand for wind power has grown, and this has increased wind turbine blade (WTB) inspections and defect repairs. This paper empirically investigates the performance of state-of-the-art deep learning algorithms, namely, YOLOv3, YOLOv4, and Mask R-CNN for detecting and classifying defects by type. The paper proposes new performance evaluation measures suitable for defect detection tasks, and these are: Prediction Box Accuracy, Recognition Rate, and False Label Rate. Experiments were carried out using a dataset, provided by the industrial partner, that contains images from WTB inspections. Three variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that on average, across all proposed evaluation measures, Mask R-CNN outperformed all other algorithms when transformation-based augmentations (i.e., rotation and flipping) were applied. In particular, when using the best dataset, the mean Weighted Average (mWA) values (i.e., mWA is the average of the proposed measures) achieved were: Mask R-CNN: 86.74%, YOLOv3: 70.08%, and YOLOv4: 78.28%. The paper also proposes a new defect detection pipeline, called Image Enhanced Mask R-CNN (IE Mask R-CNN), that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset, and a Mask R-CNN model tuned for the task of WTB defect detection and classification.

Prediction of wind turbine blade trailing edge noise under various flow conditions for a passive damage detection system

2021 ◽  
Vol 263 (2) ◽  
pp. 4079-4087
Author(s):  
Murat Inalpolat ◽  
Caleb Traylor
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Health Monitoring ◽  
Detection System ◽  
Wind Turbine Blade ◽  
Flow Conditions ◽  
Trailing Edge ◽  
Structural Health ◽  
Acoustic Spectra

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.

Machine Learning Aided Prediction of Rain Erosion Damage on Wind Turbine Blade Sections

2021 ◽  
Author(s):  
Alessio Castorrini ◽  
Paolo Venturini ◽  
Fabrizio Gerboni ◽  
Alessandro Corsini ◽  
Franco Rispoli
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Energy Production ◽  
Turbine Blades ◽  
Wind Farms ◽  
Wind Turbine Blade ◽  
Wind Turbine Blades ◽  
Coating Materials ◽  
Erosion Modelling ◽  
Rain Erosion

Abstract Rain erosion of wind turbine blades represents an interesting topic of study due to its non-negligible impact on annual energy production of the wind farms installed in rainy sites. A considerable amount of recent research works has been oriented to this subject, proposing rain erosion modelling, performance losses prediction, structural issues studies, etc. This work aims to present a new method to predict the damage on a wind turbine blade. The method is applied here to study the effect of different rain conditions and blade coating materials, on the damage produced by the rain over a representative section of a reference 5MW turbine blade operating in normal turbulence wind conditions.

Design and Analysis of Wind Turbine Blades: Winglet, Tubercle, and Slotted

2013 ◽  
Author(s):  
Alka Gupta ◽  
Abdulrahman Alsultan ◽  
R. S. Amano ◽  
Sourabh Kumar ◽  
Andrew D. Welsh
Keyword(s):  
Power Generation ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Alternative Energy ◽  
Turbine Blades ◽  
Three Dimensional ◽  
Wind Turbine Blade ◽  
Wind Turbine Blades ◽  
Wind Speeds ◽  
Governing Factor

Energy is the heart of today’s civilization and the demand seems to be increasing with our growing population. Alternative energy solutions are the future of energy, whereas the fossil-based fuels are finite and deemed to become extinct. The design of the wind turbine blade is the main governing factor that affects power generation from the wind turbine. Different airfoils, angle of twist and blade dimensions are the parameters that control the efficiency of the wind turbine. This study is aimed at investigating the aerodynamic performance of the wind turbine blade. In the present paper, we discuss innovative blade designs using the NACA 4412 airfoil, comparing them with a straight swept blade. The wake region was measured in the lab with a straight blade. All the results with different designs of blades were compared for their performance. A complete three-dimensional computational analysis was carried out to compare the power generation in each case for different wind speeds. It was found from the numerical analysis that the slotted blade yielded the most power generation among the other blade designs.

Research on Wind Turbine Blade Loads and Dynamics Factors

2014 ◽  
Vol 1014 ◽  
pp. 124-127
Author(s):  
Zhi Qiang Xu ◽  
Jian Huang
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbines ◽  
Mechanical Energy ◽  
Turbine Blades ◽  
Electrical Energy ◽  
Wind Turbine Blade ◽  
Strength Analysis ◽  
Wind Turbine Blades ◽  
Turbine Wheel

Wind turbines consists of three key parts, namely, wind wheels (including blades, hub, etc.), cabin (including gearboxes, motors, controls, etc.) and the tower and Foundation. Wind turbine wheel is the most important part ,which is made up of blades and hubs. Blade has a good aerodynamic shape, which will produce aerodynamic in the airflow rotation, converting wind energy into mechanical energy, and then, driving the generator into electrical energy by gearbox pace. Wind turbine operates in the natural environment, their load wind turbine blades are more complex. Therefore load calculations and strength analysis for wind turbine design is very important. Wind turbine blades are core components of wind turbines, so understanding of their loads and dynamics by which the load on the wind turbine blade design is of great significance.

scholarly journals Preparation and Anti-Icing of Hydrophobic Polypyrrole Coatings on Wind Turbine Blade

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Bin Qu ◽  
Zhou Sun ◽  
Fang Feng ◽  
Yan Li ◽  
Guoqiang Tong ◽  
...  
Keyword(s):  
Contact Angle ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Water Contact Angle ◽  
Turbine Blades ◽  
Wind Turbine Blade ◽  
Wind Turbine Blades ◽  
Water Contact

This paper describes the method of preparing strong hydrophobic polypyrrole (PPy) on wind turbine blades. The water contact angle of strong hydrophobic PPy coatings was 127.2°. The strong hydrophobic PPy coatings exhibited excellent anti-icing properties. The maximum icing weight of strong hydrophobic PPy coating blade was almost 0.10 g while the maximum icing weight of no coating blade was found to be 26.13 g. The maximum icing thickness of a strong hydrophobic PPy coating blade was only 1.08 mm. The current research will provide a better technique to create anti-icing coatings on wind turbine blades and other outdoor equipment.

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