Construction of Smart Grid Optimization Dispatching System Based on Improved YOLOv5 Algorithm

In actual operation, the power grid faces the problems of high loss rate and long power outage time. This paper builds an optimized dispatching system to address these problems. This paper first optimizes the YOLOv5 algorithm, and then introduces a four-scale feature fusion structure to improve the object detection effect by fusing deep semantics with shallow semantic information. The Transformer encoder module is embedded in the C3 module to strengthen the correlation between pixel blocks and improve the model's performance against complex backgrounds and target occlusion. The CIoU loss function is also used in this paper, taking into account factors such as the shape of the object. Based on the optimized YOLOv5 algorithm, this paper builds an optimized dispatching system for smart grids. The data acquisition module collects images and load data of the equipment, the image processing module preprocesses the image, the equipment recognition module uses the improved YOLOv5 algorithm to identify the equipment, the load detection module calculates the load situation based on the recognition results, and the optimized dispatching module formulates the optimal dispatching strategy based on the load situation. The system based on the improved algorithm has achieved significant improvements in grid loss rate and annual average power outage time compared to the system under the traditional algorithm. The average loss rate has been reduced to 3.2704%, and the annual average power outage time does not exceed 5.83 hours, effectively improving the operating efficiency and safety of the power grid.