In view of the low efficiency of the traditional manual evaluation method of substation equipment status under the background of complex environment, a panoramic evaluation method of substation equipment health status based on multisource monitoring and deep convolution neural network under edge computing architecture is proposed. Firstly, a panoramic sensing system for substation equipment is built based on edge computing, and an edge computing server is deployed in the substation to process the massive data obtained from multisource monitoring nearby. Then, the improved YOLOv4 network is used to detect the equipment state in the substation, in which the Squeeze-and-Excitation attention module and deep separable convolution are used to optimize the YOLOv4 network. Finally, based on the status image of substation equipment, the health status of equipment is evaluated on the panoramic platform of substation combined with the characteristics of multisource data, and four states are divided according to the evaluation criteria. Based on the selected dataset, the experimental analysis of the proposed method is carried out. The results show that the index values of accuracy, recall, and mean precision are 91.53%, 93.07%, and 92.28%, respectively. The overall performance is better than other methods and has certain application value.
An accurate perception of the state of smart substation equipment is a strong guarantee for the reliable operation of the large power grid. This article proposes using deep learning for the device condition monitoring and analysis method in a power internet of things cloud edge collaboration mode. The speeded up robust features (SURF) feature detector is used at the edge of the network to accurately collect the interest points from the image data set, providing a reliable and complete sample data set support for the cloud-based deep learning network. Adding the attention mechanism module to the cloud improves the Yolov5 network model, enhance feature extraction, and increase the monitoring and analysis capabilities of the equipment. The simulation results show that the proposed method has achieved a recall rate of 91.21% and an accuracy rate of 90.54% for insulator fault evaluation indicators.
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