Application of Fault Overlay Method and CNN in Infrared Image of Detecting Inter-Turn Short-Circuit in Dry-Type Transformer

Abstract

Inter-turn short-circuit (ITSC) faults do not necessarily produce high temperatures but have special heat distribution and characteristics. Therefore, a new recognition solution for diagnosing faults is proposed, based on the fault overlay method, and the convolutional neural network (CNN) is trained to achieve the automatic identification of infrared images. In this method, through the coverage of layers, the proposed image augmentation method is carried out and simulates the fault data of increasing training. We produce 43 fault traces through the fault overlay method on the three-phase winding of a transformer and use 90 infrared images of transformers in normal operation combined with them to enhance the amount of image data. The fault recognition ability is realized based on CNN model training, including analysis of experimental results of grayscale and color images, and Gaussian noise. In the test of the practical case, a short-circuit test of the 11.4 kV dry-type transformer is carried out, and the ITSC fault is identified when the load is about 15%. The fault characteristic block on this thermal image is 36.3 degrees, which verifies the identification available by this method, and has a certain reference value for the development of infrared image diagnosis technology for power equipment.