Blades in strong wind conditions are prone to various failures and damage that is due to the action of random variable amplitude loads. In this study, we analyze the failure of 1.5 MW horizontal axis wind turbine blades. The computational fluid dynamics unsteady calculation method is used to simulate the aerodynamic load distribution on the blade. Fluid–structure coupling methods are applied to calculate the blade stress. The results show that the equivalent stress of the blade is the largest when the azimuth angle is 30°, and the maximum equivalent stress is 20.60 MPa. There are obvious stress peaks in six sections, such as r/R = 0.10 (the span length of blade/the full length of the blade = 0.10). The frequency of damage that is caused by the stress in each area of the blade is determined based on the blade damage. The frequency of gel coat cracking in the blade tips and leaves is 77.78% and 22.22%, respectively, and the frequency of crack occurrence is 87.75%, 10.20% and 2.05%, respectively. By combining the stress concentration area and the damage results, the cause of blade damage is determined, which can replace the traditional inspection methods and improve the inspection efficiency.

To address the low wind energy utilization efficiency of vertical axis wind turbines (VAWTs) and enhance their engineering applicability, cavity and groove-flap structures were incorporated into turbine blades. Numerical simulations were performed to optimize these configurations, followed by wind tunnel experiments investigating output power variations of three VAWT types under different wind speeds at installation angles of 0°, 2°, 4°, and 6°. The Omega criterion was employed to comparatively analyze vortex evolution patterns at the leading and trailing edges for installation angles of 0°, 3°, and 5°. Experimental results demonstrated nonlinear growth in output power with increasing wind speed and rotational velocity, with groove-flap VAWTs exhibiting superior performance. The optimal installation angle was identified within 2.5–3.5°, where appropriate angles reduced adverse pressure gradients, delayed boundary layer separation, and mitigated vortex shedding effects. Excessive angles induced vortex accumulation and wake disturbances, compromising flow field stability. This study provides critical insights for optimizing VAWT aerodynamic performance through structural modifications and installation angle adjustments.

Accurately and rapidly detecting damage to wind turbine blades is critical for ensuring the safe operation of wind turbines. Current deep learning-based detection methods predominantly employ the gathered blade images directly for damage detection. However, due to the slender geometry of wind turbine blades, non-blade background information accounts for a considerable proportion of the captured images with complex background features, affecting the detection of blade damage. To address this challenge, we propose a novel edge cropping method combined with an enhanced YOLOv5s network for detecting damage in wind turbine blades, termed Edge Crop and Enhanced YOLOv5 (EC–EY). The edge cropping method adaptively modifies the cropping stride by the edge features of both sides of the blade, thereby procuring image content that predominantly encompasses the blade region. This procedure effectively mitigates the interference from complex background features and augments the utilization of image pixels. Furthermore, the enhanced YOLOv5 network incorporates the global attention mechanism into the head section of the network and substitutes the original SPPF module with an attention-based intra-scale feature interaction module. The EC–EY aims to improve the detection accuracy for small and variable-shape damages in wind turbine blades. EC–EY achieved excellent performance on a dataset of wind turbine blade damage collected in western Inner Mongolia. Notably, the edge cropping method significantly improves the accuracy of wind turbine blade damage detection.