Turn-to-Turn Short Circuit Fault Localization in Transformer Winding via Image Processing and Deep Learning Method

Abstract

Frequency response analysis (FRA) suffers from the interpretation of results despite its potential ability to detect faults related to the power transformer windings. This article presents a technique for interpreting frequency responses, which is based on image processing and a deep learning method called graph convolutional neural network (CNN). The proposed procedure transfers frequency responses into 2-D images through a visualization technique. The resulting images are aggregated into a dataset to be used as the CNN input. The proposed technique is applied on frequency responses of two different winding models with short circuit (SC) faults. The SC faults with different intensities are applied on different sections of a simulated ladder model winding and a 20 kV winding of a 1.6 MVA distribution transformer. After determining the frequency response for each faulty case and applying the visualization technique, the precise locating of the SC faults is performed by the CNN. Then, the results are analyzed by performance evaluation metrics. At this stage, the high performance of the CNN in the use of 2-D images instead of the conventional method is observed. Finally, by testing the high impedance SC faults in different sections of the simulated winding model and applying the suggested method step by step, early detection of the SC fault is also performed in this article. It should be noted that the suggested technique, in addition to its accuracy and high detection speed, can be considered as an important step in automatic interpretation of frequency responses for online monitoring of transformers.