Effect of spars cross-section design on dynamic behavior of composite wind turbine blade: Modal analysis

Tarfaoui, Mostapha; Khadimallah, H.; Shah, Owaisur Rahman; Pradillon, Jean‐Yves

doi:10.1109/powereng.2013.6635748

Cited by 4 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional methods for detecting surface defects in wind turbines rely on signals obtained from various sensors to determine whether there are faults in the turbine blades. Common detection techniques include infrared thermal imaging [1], ultrasonic detection [2], fault detection based on vibration signals [3,4], acoustic emission detection [5][6][7], and grating fiber detection [8].…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv5 Based on Multi-Strategy Integration for Multi-Category Wind Turbine Surface Defect Detection

Lei,

Wang,

Wang

et al. 2024

Energies

View full text Add to dashboard Cite

Wind energy is a renewable resource with abundant reserves, and its sustainable development and utilization are crucial. The components of wind turbines, particularly the blades and various surfaces, require meticulous defect detection and maintenance due to their significance. The operational status of wind turbine generators directly impacts the efficiency and safe operation of wind farms. Traditional surface defect detection methods for wind turbines often involve manual operations, which suffer from issues such as high subjectivity, elevated risks, low accuracy, and inefficiency. The emergence of computer vision technologies based on deep learning has provided a novel approach to surface defect detection in wind turbines. However, existing datasets designed for wind turbine surface defects exhibit overall category scarcity and an imbalance in samples between categories. The algorithms designed face challenges, with low detection rates for small samples. Hence, this study first constructs a benchmark dataset for wind turbine surface defects comprising seven categories that encompass all common surface defects. Simultaneously, a wind turbine surface defect detection algorithm based on improved YOLOv5 is designed. Initially, a multi-scale copy-paste data augmentation method is proposed, introducing scale factors to randomly resize the bounding boxes before copy-pasting. This alleviates sample imbalances and significantly enhances the algorithm’s detection capabilities for targets of different sizes. Subsequently, a dynamic label assignment strategy based on the Hungarian algorithm is introduced that calculates the matching costs by weighing different losses, enhancing the network’s ability to learn positive and negative samples. To address overfitting and misrecognition resulting from strong data augmentation, a two-stage progressive training method is proposed, aiding the model’s natural convergence and improving generalization performance. Furthermore, a multi-scenario negative-sample-guided learning method is introduced that involves incorporating unlabeled background images from various scenarios into training, guiding the model to learn negative samples and reducing misrecognition. Finally, slicing-aided hyper inference is introduced, facilitating large-scale inference for wind turbine surface defects in actual industrial scenarios. The improved algorithm demonstrates a 3.1% increase in the mean average precision (mAP) on the custom dataset, achieving 95.7% accuracy in mAP_50 (the IoU threshold is half of the mAP). Notably, the mAPs for small, medium, and large targets increase by 18.6%, 16.4%, and 6.8%, respectively. The experimental results indicate that the enhanced algorithm exhibits high detection accuracy, providing a new and more efficient solution for the field of wind turbine surface defect detection.

show abstract

Section: Introductionmentioning

confidence: 99%

Improved YOLOv5 Based on Multi-Strategy Integration for Multi-Category Wind Turbine Surface Defect Detection

Lei,

Wang,

Wang

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

Simulation of Mechanical Behavior and Damage of a Large Composite Wind Turbine Blade under Critical Loads

et al. 2017

View full text Add to dashboard Cite

Modal analysis of an iced offshore composite wind turbine blade

Lagdani¹,

Tarfaoui

Nachtane

et al. 2021

Wind Engineering

View full text Add to dashboard Cite

In the far north, low temperatures and atmospheric icing are a major danger for the safe operation of wind turbines. It can cause several problems in fatigue loads, the balance of the rotor and aerodynamics. With the aim of improving the rigidity of the wind turbine blade, composite materials are currently being used. A numerical work aims to evaluate the effect of ice on composite blades and to determine the most adequate material under icing conditions. Different ice thicknesses are considered in the lower part of the blade. In this paper, modal analysis is performed to obtain the natural frequencies and corresponding mode shapes of the structure. This analysis is elaborated using the finite element method (FEM) computer program through ABAQUS software. The results have laid that the natural frequencies of the blade varied according to the material and thickness of ice and that there is no resonance phenomenon.

show abstract

Effect of spars cross-section design on dynamic behavior of composite wind turbine blade: Modal analysis

Cited by 4 publications

References 7 publications

Improved YOLOv5 Based on Multi-Strategy Integration for Multi-Category Wind Turbine Surface Defect Detection

Improved YOLOv5 Based on Multi-Strategy Integration for Multi-Category Wind Turbine Surface Defect Detection

Simulation of Mechanical Behavior and Damage of a Large Composite Wind Turbine Blade under Critical Loads

Modal analysis of an iced offshore composite wind turbine blade

Contact Info

Product

Resources

About