Efficient bird damage prevention of transmission lines is a long‐term challenge for power grid operation and maintenance. An approach combined lightweight convolutional neural network (CNN), image processing and object detection is presented in this paper to detect typical bird species related to transmission line faults. An image dataset of 20 bird species that threaten transmission line security is constructed. The YOLOv4‐tiny algorithm model is constructed and trained combining stage‐wise training, mosaic data enhancement, cosine annealing, and label smoothing. The mean average precision (mAP) can reach 92.04% on the test set by adjusting the parameters of the training process. Then, the validity of the proposed method is verified according to the test results and performance indexes by comparing with other methods, including Faster RCNN, SSD, YOLOv4 etc. Some image pre‐processing methods such as motion blur, defocus blur, contrast and brightness adjustment are used to simulate the scenarios in practical engineering applications. The proposed method can detect bird species perched around transmission lines with high‐efficiency, which is useful for differential prevention of bird‐related outages of power grids.

Bird-related outages have become the third failure cause of overhead transmission lines. Different bird species may cause diverse line faults. In order to improve the pertinence of bird-related outage prevention, it is necessary for inspectors to identify different bird species. This paper proposes a bird recognition method based on image processing, feature extraction and machine learning. The Grabcut algorithm is used for foreground extraction of bird images. Based on fine-grained classification ideas, the bird head is selected as the discriminative part for feature extraction. The color, texture, and shape features of the bird head are respectively characterized by color moments, gray-level co-occurrence matrix (GLCM), and geometric descriptions. Altogether 25 features are extracted to characterize the bird species. Taking these features as input parameters and the corresponding bird species as outputs, an intelligent classification model is established by multi-class support vector machine (SVM). This model is used to classify five types of birds which threaten the safe operation of transmission lines. The kernel principal component analysis (KPCA) is applied for feature dimension reduction. After feature selection and model training, the recognition accuracy reaches 88%. This study provides a reference to construct a bird identification system for transmission line inspectors to recognize harmful bird species.

Bird activities including the defecation, nesting, and flight are easy to cause electrical faults of overhead transmission lines, study on the prevention measures of bird damage to transmission lines is of great significance to ensure the stable and reliable operation of the power system. With the establishment of finite element simulation model, the effects of box of bird guard with different material on the electric field and potential distribution on transmission line insulators are studied. Besides, combined with the theory of air discharge and the results of simulation, the influence of rainwater on the air gap around the insulator breakdown characteristics is studied when the water is drained from a scupper at the bottom of the box of bird guard, and a reasonable design scheme for the box of bird guard scupper is proposed. The research results here can provide a guidance for the selection and key parameters’ design of the box of bird guard.

With large cross-domains and long distances, overhead transmission lines are easily affected by various natural disasters such as lightning strikes, external damage and bird trouble in complex terrains, resulting in line tripping fault. In this paper, fault characteristics of overhead transmission lines are studied and analyzed, and it is found that different fault types of overhead transmission lines have different characteristics in time, reclose and fault recording. A line tripping fault was analyzed, and it verified the effectiveness of the fault identification technology for overhead transmission lines. Based on the study on fault characteristics of overhead transmission lines, operation and maintenance personnel can quickly find the cause of fault tripping by using fault feature identification technology and it is good for improving the reliability of transmission line operation through targeted management and technical means.

Bird activities are easy to cause electrical faults of overhead transmission lines. As one of the main anti bird device, baffle of bird guard has been widely used in China. The performance of baffle of bird guard used in 220 kV transmission line are analyzed in this paper, with the establishment of finite element simulation model, the effects of the baffle of bird guard with different materials on the transmission line insulator electric field and potential distributions are studied. Besides, considering that some birds may build nests on the baffle of bird guard in practical applications, and part of the material to build bird's nest may fall off, which may lead to the discharge of the transmission line insulator, combined with the theory of air discharge and results of the simulation, the influence of external object approaching on the discharge of the insulator is studied, and a reasonable structure design scheme of the baffle of bird guard is put forward. The research results in this paper can provide a guidance for the selection and key parameters design of the baffle of bird guard.