Classification of wind turbine blade incidents regarding lightning risk management

2014 ◽  
Author(s):  
Yoh Yasuda ◽  
Toshiaki Fujii ◽  
Kazuo Yamamoto ◽  
Nobuyuki Honjo ◽  
Shigeru Yokoyama
Keyword(s):  
Risk Management ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade

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.

Sign in / Sign up

Export Citation Format

Share Document