Icing estimation on wind turbine blade by the interface temperature using distributed fiber optic sensors

2020 ◽  
Vol 27 (6) ◽  
Author(s):  
Zhaohui Zhang ◽  
Wensong Zhou ◽  
Hui Li
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Fiber Optic ◽  
Fiber Optic Sensors ◽  
Wind Turbine Blade ◽  
Interface Temperature

scholarly journals Wind Turbine Blade Monitoring with Brillouin-Based Fiber-Optic Sensors

2017 ◽  
Vol 2017 ◽  
pp. 1-5 ◽  
Author(s):  
Agnese Coscetta ◽  
Aldo Minardo ◽  
Lucio Olivares ◽  
Maurizio Mirabile ◽  
Mario Longo ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Fiber Optic ◽  
Strain Sensor ◽  
Experimental Tests ◽  
Fiber Optic Sensors ◽  
Composite Blade ◽  
Operation Stability ◽  
Distributed Strain ◽  
Strain Detection

Wind turbine (WT) blade is one of the most important components in WTs, as it is the key component for receiving wind energy and has direct influence on WT operation stability. As the size of modern turbine blade increases, condition monitoring and maintenance of blades become more important. Strain detection is one of the most effective methods to monitor blade conditions. In this paper, a distributed fiber-optic strain sensor is used for blade monitoring. Preliminary experimental tests have been carried out over a 14 m long WT composite blade, demonstrating the possibility of performing distributed strain and vibration measurements.

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 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.

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