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Wind energy is an important clean energy, according to China's carbon peak and carbon neutral strategy, the policy of replacing small with large wind turbine blades and replacing old with new is imperative. The main material of wind turbine blades is glass fiber reinforced polymer, which has problems of difficult recycling technology and high recycling cost. Nowadays, there is no ideal large-scale recycling method. Therefore, the systematic and high-value utilization of waste wind turbine blades is imminent. In this paper, the material type, recycling methods and their respective characteristics of wind turbine blades are summarized. The recent developments of waste wind turbine blades in cement-based materials and thermoplastic polymer materials are thoroughly discussed. Overall, this paper can provide reference for the subsequent development and research on the recycling waste wind turbine blades. [ABSTRACT FROM AUTHOR]

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Online early detection of surface damages on blades is critical for the safety of wind turbines, which could avoid catastrophic failures, minimize downtime, and enhance the reliability of the system. Monitoring the health status of blades is attracting more and more attention including on-site cameras and mobile cameras by drones and crawling robots. To deploy fast and efficient damage detection methods from image data, this work presents a hierarchical identification framework for wind turbine blades, which consists of a Haar-AdaBoost step for region proposal and a convolutional neural network (CNN) classifier for damage detection and fault diagnosis. Case studies are carried out on real data set collected from an eastern China wind farm. Results show that (i) the proposed framework can detect and identify the blade damages and outperforms other schemes include SVM and VGG16 models, (ii) sensitive analysis is conducted to validate the robustness of proposed method under limited data conditions, (iii) the proposed scheme is faster than one-step CNN method that directly classifying raw data. • Hierarchical damage identification framework using images for wind turbine blades. • Classifying defects by Deep CNNs using proposed regions by AdaBoost cascade classifier. • Verified efficient and accurate workflow by balancing false alarm and missed detection. • End-to-end scheme with no need of handcrafted features and applicable to edge devices. [ABSTRACT FROM AUTHOR]

• The infrared nondestructive testing experiment under natural excitation is carried out on the blades with wear damage, and the best light conditions are obtained. • Through the physical modeling method of fluid-thermal-solid coupling, the simulation of scene reconstruction is carried out, and the experimental results are in good agreement with the simulation results. • Based on the simulation model, the influence of wind speed on the temperature, pressure and stress at the damaged blade is studied. At the same time, from the actual project, the influence of wind direction on the temperature, pressure and stress at the damaged blade is also studied. In recent years, with the increase of wind power generation and the rapid growth of the number of wind turbines installed, the safety monitoring of wind turbine blades has also attracted wide attention. To further study the thermal effect of wind turbine damaged blades under natural excitation in summer in northern China, based on the outdoor infrared non-destructive testing of wind turbine blades, a numerical simulation of wind turbine blades with wear damage is carried out by using the fluid solid heat transfer theory in COMSOL multi-physical field coupling software. The best detection light condition is obtained, in which the detection effect of wear damage is the best when the light intensity reaches 1000 W.m−2 or above at noon. The simulation results are basically in agreement with the experimental results. And compared with the previous physical modeling method using natural convection heat transfer coefficient to replace wind speed, the physical modeling method considering wind speed and air humidity is more consistent with the experimental results, the maximum error value was decreased by 7.46 %, which verifies the rationality of the model and the feasibility of the method. In addition, the blade works in the natural environment for a long time, the thermal stress will have a significant impact on its fatigue strength, so on the basis of this model, the coupling analysis, and then analyze the change of thermal stress in different wind speed. With the increase of wind speed, the temperature on the blade surface gradually decreases and the wind pressure gradually increases. Because the influence of temperature on the stress is greater than that of wind pressure, the stress also shows a gradual downward trend. [ABSTRACT FROM AUTHOR]