Transformer Fault Detection Algorithm Based on Computer Vision and Deep Learning Technology

With the expansion of the installed capacity of power system and the scale of power grid, the operation of various electrical equipment requires the reliability of power system to be improved continuously. Transformer is one of the key equipments in the power distribution system, and its running state is directly related to the reliability of the whole power system. Condition-based maintenance technology is to diagnose the equipment by using maintenance technology when the equipment is charged. On-line detection and fault detection technology of transformer can improve the reliability of equipment, reduce the maintenance cost of equipment and improve economic benefits. In this paper, a transformer fault detection algorithm based on computer vision and deep learning technology is proposed, and the effectiveness and feasibility of transformer computer vision fault online detection technology are verified from two aspects: fault detection scheme design and transformer fault detection system. The simulation results show that with the increase of the number of experiments, the accuracy of this algorithm is stable at about 95%, and the real-time wavelength tends to be stable, which is 15.85% higher than the traditional algorithm on average. This method can effectively detect the transformer fault, and at the same time, it can detect the latent fault that leads to the drift of transformer winding parameters.