scholarly journals Defect identification of wind turbine blade based on multi‐feature fusion residual network and transfer learning

2021 ◽  
Author(s):  
Jiawei Zhu ◽  
Chuanbo Wen ◽  
Jihui Liu
Keyword(s):  
Wind Turbine ◽  
Transfer Learning ◽  
Turbine Blade ◽  
Feature Fusion ◽  
Wind Turbine Blade ◽  
Residual Network ◽  
Defect Identification

Advances in wind turbine blade design and materials

2013 ◽  
Author(s):  
Povl Brøndsted ◽  
Rogier P. L. Nijssen
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Blade Design ◽  
Turbine Blade Design

Noise analysis of the wind turbine blade

2013 ◽  
Author(s):  
Gwochung Tsai ◽  
Yita Wang ◽  
Yuhchung Hu ◽  
Jaching Jiang
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Noise Analysis ◽  
Wind Turbine Blade

DYNAMIC ANALYSIS OF THE FLUID FLOW OVER A WIND TURBINE BLADE

2017 ◽  
Author(s):  
Aldemir Ap Cavalini Jr ◽  
João Marcelo Vedovoto ◽  
Renata Rocha
Keyword(s):  
Fluid Flow ◽  
Dynamic Analysis ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade

scholarly journals Design of deicing device for wind turbine blade based on microwave and ultrasonic wave

2021 ◽  
Vol 1748 ◽  
pp. 062018
Author(s):  
Ruiliang Luo ◽  
Jie Mei ◽  
Xu Chen ◽  
Jinyu Guo
Keyword(s):  
Wind Turbine ◽  
Ultrasonic Wave ◽  
Turbine Blade ◽  
Wind Turbine Blade

Determination of the geometric and material properties of the NREL Phase VI wind turbine blade

2021 ◽  
Author(s):  
John H. Chujutalli ◽  
Guilherme Pimenta da Silva ◽  
Segen F. Estefen
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Material Properties ◽  
Wind Turbine Blade ◽  
Nrel Phase Vi

Carbon footprint and embodied energy of a wind turbine blade—a case study

2021 ◽  
Author(s):  
Antonio Augusto Morini ◽  
Manuel J. Ribeiro ◽  
Dachamir Hotza
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Carbon Footprint ◽  
Wind Turbine Blade ◽  
Embodied Energy

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.

Impact of dielectric barrier discharge plasma on the wake of a wind turbine blade section oscillating in plunge

2021 ◽  
pp. 095765092110337
Author(s):  
GH Maleki ◽  
Ali R Davari ◽  
MR Soltani
Keyword(s):  
Wind Turbine ◽  
Dielectric Barrier Discharge ◽  
Turbine Blade ◽  
Discharge Plasma ◽  
Barrier Discharge ◽  
Wind Turbine Blade ◽  
Plasma Actuators ◽  
Dielectric Barrier Discharge Plasma ◽  
Dielectric Barrier ◽  
Barrier Discharge Plasma

Effects of dielectric barrier discharge plasma have been studied on the wake velocity profiles of a section of a 660 kW wind turbine blade in plunging motion in a wind tunnel. The corresponding unsteady velocity profiles show remarkable improvement when the plasma actuators were operating and the angles of attack of the model were beyond the static stall angles of the airfoil. As a result the drag force was considerably reduced. It is further observed that the plasma-induced flow attenuates the leading edge vortices that are periodically shed into wake and diminishes the large eddies downstream. The favorable effects of the plasma augmentation are shown to occur near the uppermost and lowermost positions of the plunging paths where the wake is primarily dominated by the vortices of the same sign. The wake structure in the presence of the flow induced by the plasma actuators shows that the actual effective angles of attack seen by the plunging airfoil reduces in comparison with that for the case of the plasma augmentation off situation.

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