Identifying the equipment in the distribution station area is necessary. It can not only provide detailed grid connection data for grid upgrading and new energy

but also effectively improve the low-voltage operation level of the distribution network. In this paper

a method of equipment identification in the distribution station area is proposed

which combines the topological structure characteristics of the equipment in the distribution station area

the steady-state characteristics of the power time series data and the lifetime accuracy characteristics of the equipment in the distribution station area. The CenterNet algorithm is deployed at the edge equipment to realize unsupervised learning for equipment identification in the distribution area. Considering the lack of computing power of embedded devices

the CenterNet algorithm adopts a variety of lightweight processing

including reducing the size of input data

optimizing the network structure and reasoning structure

pruning and other operations

so that the data detection speed is increased from 1720 ms to 322 ms

which can achieve the goal of real-time processing on the premise of meeting the accuracy requirements and provides an effective means for the subsequent design and optimization of the distribution station area.