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Abstract Bird‐related outages greatly threaten the safety of overhead transmission and distribution lines, while electrocution and collisions of birds with power lines, especially endangered species, are significant environmental concerns. Automatic bird recognition can be helpful to mitigate this contradiction. This paper proposes a method for automatic classification of bird species related to power line faults combining deep convolution features with error‐correcting output codes support vector machine (ECOC‐SVM). An image dataset of about 20 high‐risk and 20 low‐risk bird species was constructed, and the feed‐forward denoising convolutional neural network was used for image preprocessing. The deep convolution features of bird images were extracted by DarkNet‐53, and taken as inputs of the ECOC‐SVM for model training and bird species classification. The gradient‐weighted class activation mapping was used for visual explanations of the model decision region. The experimental results indicate that the average accuracy of the proposed method can reach 94.39%, and its performance was better than other models using different feature extraction networks and classification algorithms.

Wind turbine blades are susceptible to various types of damage caused by environmental factors, external forces, and other influences. Achieving efficient and accurate detection of damage defects in wind turbine blades is essential to extend the service life of wind turbines. To this end, this paper proposes a cascaded adaptive hybrid attention network (CAHAN). Firstly, a high-resolution wind turbine blade damage defects image dataset is constructed using the dual aggregation transformation (DAT) model and the contrast enhancement algorithm. Secondly, a global contextual attention pyramid (GCAP) module is proposed to effectively integrate global and local feature information. Then, a cascaded adaptive hybrid attention (CAHA) module is developed to design a PANet feature fusion network, which can effectively filter out redundant global feature information. In addition, the dynamic head (DH) modules covering scale, space, and task awareness are applied to wind turbine blade defects detection, greatly enhancing the model’s capability to detect blade damage defects. Finally, the proposed method is tested using wind turbine blade defect images collected from open web downloads. The results show that the CAHAN model achieves an overall detection accuracy of 97.9% and boasts a fast detection speed of 149.25 frames per second (FPS), which has superior overall performance compared to other object detection models. This method is useful for defect detection of wind turbine blade damage.

During the construction of cable joints, three common defects may occur on the XLPE surface: scratches, moisture exposure, and adhered contaminated particles. To evaluate the impact of these defects on joint performance, this paper establishes a sheet model of XLPE insulation in cable joints to analyze the changes in the electric field under different defects and explore the influence of different defects on the electric field and breakdown voltage. Results of the study reveal that the electric field at the scratch site on XLPE produces severe distortion, being 1.6 times that of non-scratch areas. When exposed to moisture, the more conductive impurities present in the adhered contaminated water on the XLPE surface, the higher the conductivity of the contaminated water, thereby increasing its conductive performance and the electric field strength, which is 1.22-1.4 times that of the non-moist interface. When particles adhere to the XLPE surface, severe distortion occurs at the particle-interface electric field, approximately 1.5 times that of the defect-free interface. Scratches have the most significant impact on the electric field of XLPE insulation. Experimental results also demonstrate that the breakdown voltage without defects is 129.6 kV, while the breakdown voltage with scratch defects is 59.1 kV, moisture defects is 69.7 kV, and particle contamination defects is 59.2 kV, with scratches having the most significant impact on the breakdown voltage of XLPE insulation. These findings provide important insights into the influence of different defects on the insulation performance of cable joints. [ABSTRACT FROM AUTHOR]