Bolts are important components on transmission lines, and the timely detection and exclusion of their abnormal conditions are imperative to ensure the stable operation of transmission lines. To accurately identify bolt defects, we propose a bolt defect identification method incorporating an attention mechanism and wide residual networks. Firstly, the spatial dimension of the feature map is compressed by the spatial compression network to obtain the global features of the channel dimension and enhance the attention of the network to the vital information in a weighted way. After that, the enhanced feature map is decomposed into two one-dimensional feature vectors by embedding a cooperative attention mechanism to establish long-term dependencies in one spatial direction and preserve precise location information in the other direction. During this process, the prior knowledge of the bolts is utilized to help the network extract critical feature information more accurately, thus improving the accuracy of recognition. The test results show that the bolt recognition accuracy of this method is improved to 94.57% compared with that before embedding the attention mechanism, which verifies the validity of the proposed method.

The accurate detection of insulators is an important prerequisite for insulator fault diagnosis. To solve the problem of background interference and overlap caused by the axis-aligned bounding boxes in the tilting insulator detection tasks, we construct an improved detection architecture according to the scale and tilt features of the insulators from several perspectives, such as bounding box representation, loss function, and anchor box construction. A new orientation detection method for tilting insulators based on angle regression and priori constraints is put forward in this paper. Ablation tests and comparative validation tests were conducted on a self-built aerial insulator image dataset. The results show that the detection accuracy of our model was increased by 7.98% compared with that of the baseline, and the overall detection accuracy reached 82.33%. Moreover, the detection effect of our method was better than that of the YOLOv5 detection model and other orientation detection models. Our model provides a new idea for the accurate orientation detection of insulators.

The classification of transmission tower bolt images faces challenges such as class imbalance, sample scarcity, and the low pixel proportion of pins. Traditional classification methods exhibit poor performance in identifying key categories with small proportions, fail to leverage the correlation between transmission line fittings and bolts, and suffer from severe false positive issues. This study proposes a novel approach that dynamically integrates two sampling strategies to address the class imbalance problem while incorporating contrastive learning and category labels to enhance the discrimination of easily confused samples. Additionally, an auxiliary branch discrimination mechanism effectively exploits the correlation between fittings and bolts and, combined with a threshold-based decision process, significantly reduces the false positive rate (by 3.74%). The experimental results demonstrate that, compared to the baseline SimCLR framework with ResNet18, the proposed method improves accuracy (Acc) by 10.22%, reduces the false alarm rate by 5%, and significantly enhances classification reliability in transmission line inspections, thereby mitigating unnecessary human resource consumption.

Due to the unpredictability of loads and the complexity of fault propagation, modern power distribution networks require advanced technologies for fault identification and localization. In traditional systems, there is a high rate of false alarms, slow response times, and limited precision. For fault identification, the Multimodal Deep Feature Hybrid Deep Learning Model (MDF-HDL) utilizes LiDAR, optical images, and sensor data. This model was developed to help overcome these obstacles. The model utilizes deep learning layers to provide elaborate, multimodal feature representations. Additionally, Kalman filtering is used to enhance feature fusion. Classification results can be refined using decision trees, which are optimized using the Adam algorithm. This helps to reduce mistake rates. Through the use of GIS mapping, faults are precisely identified, facilitating efficient maintenance planning. While maintaining a low computational complexity, the MDF-HDL model, implemented in Python, achieves an accuracy of 98.91%, a precision of 98.7%, a recall of 98.3%, an F1-score of 98.5%, and an inference time of 12.5 milliseconds. Through the incorporation of multimodal data and sophisticated algorithms, the system is able to transcend standard constraints, guaranteeing fault management that is both dependable and effective in complicated grid contexts.