Distribution line insulators play a key role in electrical insulation and supporting lines in distribution lines. Insulator defects due to overvoltage, thermal stress, and environmental pollution may trigger power transmission instability and line collapse, thus threatening the safe operation of distribution networks. However, distribution line insulators often present detection challenges due to their compact dimensions, diverse flaw types, and frequent installation in populated areas with visually cluttered environments. The combination of these factors, including small defect sizes, varying failure patterns, and complex background interference, in both urban and rural settings, creates significant difficulties for precise defect identification in these critical components. In response to these challenges, this paper proposes a defect recognition algorithm for distribution line insulators based on the improved YOLO11 model. Firstly, the algorithm combines the detection head of the original model with the Adaptively Spatial Feature Fusion (ASFF) module to effectively fuse defect features at different resolution levels and improve the model’s ability to recognize multi-scale defect features. Secondly, a Bidirectional Feature Pyramid Network (BiFPN) replaces the FPN + PAN structure of the original model to achieve a more effective transfer of contextual information in order to facilitate the model’s efficiency in performing defect feature fusion, and the Convolutional Block Attention Module (CBAM) Attention mechanism is embedded in the BiFPN output so that the model is able to give priority attention to defective features on insulators in complex recognition environments. Finally, the ShuffleNetV2 module is used to reduce the parameters of the improved model by replacing the large-parameter C3k2 module at the end of the backbone network for easy deployment on lightweight and small devices. The experimental results show that the improved model performs well in the distribution line insulator defect detection task, with an accuracy precision (AP) and mean accuracy precision (mAP) of 97.0% and 98.1%, respectively, which are 1.4% and 0.7% higher than the original YOLO11 model.

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 graph stream is defined as rapid edge streams on a huge domain of nodes. Nowadays, graph streams play important roles in network traffic, social networks, and cloud troubleshooting. Therefore, various summary structures for graph streams are proposed to obtain approximate evaluation results. However, these structures either sacrifice accuracy for guaranteed throughput or compromise memory consumption for high precision. In view of the limitations, we propose Cuckoo Matrix. It only uses one adjacency matrix to complete high accuracy queries while assuring large throughput. Meanwhile, Cuckoo Matrix is capable of preserving the connectivity of edges for the purpose of supporting both structural queries and weight-based estimations. The experimental results show that Cuckoo Matrix improves insertion throughput by 25% and reduces memory consumption by 25% compared to the state-of-the-art, which meets the current requirements of graph stream summarization.

This work presents an improved self-adaptive power distribution approach for the microgrid in five modes under different pandemic conditions in a typical tourism water village in Northern China. Differently from the other studies, this work concentrates on satisfying the specific power supply requirements under the COVID-19 background, with the maximum value of the composite index as the object function. Composite index includes not only the economic factors, but also some compulsive factors to ensure the requested power supply of the residents/tourists. The improved particle swarm optimization method which employs the modified weighted factor and the elite strategy is utilized to optimize the power dispatching of the microgrid. Moreover, the impact of the pandemic has been fully considered by comparing the power dispatching before and after the pandemic. The case study in Baiyangdian Region confirms the effectiveness of the proposed method. With this method, the optimal power dispatching is determined under different modes.