Wind turbine blades can be damaged by various factors, such as lightning, fatigue loads, icing, erosion, and impacts123. These factors can cause wear, fatigue, and corrosion of the blade materials and structures, leading to reduced performance, increased maintenance costs, and unit failure123. Wind turbine blades are exposed to complex environmental and mechanical loading during their service time, which can affect their durability and reliability23.
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In this paper, a lightweight wind turbine blade damage detection network MC-YOLO is proposed to overcome the problems of the current wind turbine blade defect detection algorithm, for
Using a deep learning target detection algorithm to detect blade damage earlier in wind turbines allows for improving the detection efficiency and reducing the wind turbines'' maintenance costs, accounting for 25–30% of the
Compared to vibration-based methods, damage detection of wind turbine blades with images or videos is probably more sensitive for small defects that do not cause significant
Therefore, the health monitoring and damage identification of wind turbine blades have become a main research focus. At present, in addition to the overview of various detection methods of wind turbine blades, there is a
results of static and fatigue testing of wind turbine blades being used by different laboratories, and to bring the international laboratories closer to a unified approach. Since NREL began blade
Wind power has become indispensable part of renewable energy for its efficient and economical power generation method. Meantime, the risk of lightning damage on the wind turbine blades
This study revealed a simplified method of determining equivalent-damage test loads without any specific knowledge of the test blade structure or geometry. The only inputs were the fatigue
Papadakis et al. have introduced a comprehensive analysis of damage to wind turbine blades since they are the structure's most intensively stressed components .
These types of damage negatively affect the performance of wind turbines, with direct economic impacts stemming from both the shutdown of the damaged wind turbines for repair (or at least the low-efficiency operation in case of minor damage) and, of course, the cost of repair itself.
If oil stains exist on the blade’s surface for a long time, the blade will be more susceptible to erosion by external environmental factors such as wind, sand, and rain, which will cause more severe failures; Crack: Cracks are a common surface damage type of wind turbine blades during their operation.
The traditional methods to detect wind turbine blade damage involve manual visual inspections, which are inefficient, inaccurate, and costly [ 4 ]. With the rapid development of computer vision technology, new ideas for intelligent industrial damage detection have emerged [ 5 ].
There is no standard wind turbine blade damage or defect categorization system. Various categorization systems have been developed and are in use by turbine or blade manufacturers, service providers and blade inspection/maintenance companies, drone operators, turbine owners and operators, consultants, and industry groups/consortia.
Damage to wind turbine blades due to fatigue can be prevented with two alternative approaches: appropriate selection of the wind park’s installation site and the optimum siting of the wind turbines.
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