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Wind energy, as a renewable energy source, is becoming increasingly important. The maintenance and damage detection of wind turbine blades are particularly crucial. For this purpose, the study aims to optimize the You Only Look Once (YOLO) processing algorithm for drone images to improve the detection efficiency. Firstly, the damage images captured by drones are preprocessed and optimized, including deblurring, noise reduction, and image enhancement. Subsequently, the YOLOv5 model is improved in terms of structure and regression function, and a novel damage detection model is proposed. The research results indicated that the minimum loss function value of the improved model was 2.75, the average accuracy was 95 %, and the highest intersection over union was 91 %. After simulation testing, the detection effect of this model on abrasion, crackle, edge cracking, and coating peeling images was significantly better than other models in the same series. Its average time was as short as 2.43 s, reaching a maximum frame rate of 35.46. From this, the combination of drone image technology and improved image processing algorithm has a positive impact on improving the operational efficiency and safety of wind turbine blades. Compared with the traditional methods, the proposed model has significant advantages in terms of accuracy and real-time performance of damage detection, providing a new technical means for efficient maintenance of wind turbines. Meanwhile, the method shows high robustness and reliability in different types of damage detection, demonstrating the extensive potential in practical applications.

Pyrolysis technology has recently made significant progress in recycling millions of tons of wind turbine waste (WTB). Short fibers represent its main product (∼70 wt%), which needs further studies to explore its future applications. In this context, this work aims to the valorization of the recycled glass fiber (rGF) derived from pyrolysis of WTB (at 550 °C) in the production of rGF-reinforced mortar composite (rGF/M) for sustainable building applications. The research began by subjecting the derived rGF to sieving (rGFs), washing (rGFw), and oxidation (rGFo) pretreatments for purification and removing any impurities, organic residues, and char particles. Afterward, the chemical degradation of the treated rGF in a strong alkaline cement solution with pH = 14 was studied for up to 90 days. In parallel, the mortar composite samples were fabricated at 1 wt% of the treated rGF batches (rGFr, rGFw, and rGFo) to study the effect of different pretreatments on their microstructure, dispersion, compressive strength, flexural strength, and water absorption capabilities behavior after different curing periods (28 and 90 days). The results showed that rGFo (7.84%) showed a lower degradation rate versus rGFw (16.41%) and rGFr (30.76%). Meanwhile, the highest curing period (90 days) contributed to improving the properties of the mortar composites in general, especially in the case of using rGFo which led to improved compressive strength (15%) and reduced absorption coefficient (38%) and cumulative water content (32%) with a uniform distribution of short fibers in the matrices and a slight increase in flexural strength compared to control mortar sample (8.6 MPa). While rGFs provides better flexural strength (9.3 MPa) with a 12% improvement. Based on these results it can be concluded that rGFo has high potential in sustainable building materials with high competition with virgin glass fiber.

ABSTRACT The Additively‐Manufactured, System‐Integrated Tip (AMSIT) project is leveraging the flexibility of 3D printing to integrate several technologies in a wind turbine blade tip, while reducing the levelized cost of electricity (LCOE) produced. The design integration is demonstrated for a 200‐kW–scale turbine with 13‐m blades, with the outer 15% of the blade replaced with a 3D‐printed design. Aerodynamic performance is enhanced without increasing swept area through inclusion of a winglet and surface texturing, both challenging for traditional manufacturing. Longevity and durability are improved through integrated lightning and leading edge erosion protection. Increased power, reduced repair frequency, and ease of repair through blade modularity all contribute to reduced LCOE. The analysis is also extended to modern MW‐scale designs to estimate the impact of the technology at scale, demonstrating the potential to reduce LCOE significantly for modern onshore turbines, with even higher potential savings offshore.

ObjectiveStructural optimization of wind turbine blades is one of the most important ways to enhance performance, reduce mass and lower costs. Currently, the research on structural optimization focuses on the blade size or layup scheme, and the overall configuration inside the blade is relatively fixed, but it also limits the structural innovation to a certain extent.MethodsTherefore, Abaqus softwave topology optimization module was adopted to optimize the design of the blade web structure to seek for the optimal material layout, to obtain a new blade structural configuration to achieve the purpose of lightweighting.ResultsThe results show that the mass of the web plate is reduced by about 4.56% after optimization; the optimization of the blade web topology does not affect the overall mass distribution and stiffness distribution of the blade, and maintains good vibration resistance; the maximum stress of the optimized web plate increases, but it does not yet exceed the maximum permissible stress of the material; the flexure factor of each order decreases, which is due to the reduction of the material of the web plate to weaken the role of its support for the surface of the blade, but it still remains in the safe range.

A novel approach for detecting small targets of wind turbine blade surface damage, particularly minute defects within intricate scenes depicted in low-resolution images, is introduced through the utilization of an enhanced YOLO-v7 algorithm. The method incorporates the Ghost-Shuffle Convolution (GSConv) to improve detection accuracy without compromising inference speed, tailored to the specific characteristics of wind turbine blade defects. Furthermore, the integration of the Simple Attention Mechanism (SimAM) within the ELAN structure aims to enhance the identification of smaller wind turbine blades. To expedite model convergence, the Edge Intersection over Union (EIoU) is adopted as the edge loss function. Experimental findings demonstrate that the refined algorithm attains an average accuracy of 78.7%, surpassing the original YOLO-v7 algorithm by 4.2%. Moreover, it achieves a recognition speed of 105.1 frames per second, facilitating more effective detection of wind turbine blade defects while meeting real-time target detection requisites.