Distribution grid planning and surveying based on UAV image acquisition and optimization algorithm

The current location selection of distribution network equipment is influenced by multiple factors such as terrain differences and regional electricity demand, resulting in traditional manual site selection meeting the safety requirements of power supply. Based on this, research is conducted on optimizing the location selection of transformers through U-Net drone image segmentation survey technology and improved K-means clustering algorithm. The experimental findings denote that the U-Net drone image segmentation survey technology maintains an average accuracy of 94.25% in image segmentation of different terrains, which is higher than other models. The average voltage of residential area nodes under the optimized transformer placement method is 0.96p.u., which is higher than the average voltage of traditional site selection methods. The experiment shows that the proposed method can meet the site selection requirements for new transformers in distribution network planning, thereby providing scientific support for the safe operation of the distribution network.