Detection of Inter-Turn Faults in Transformers Using Continuous Wavelet Transform and Convolutional Neural Network

Abstract

This paper presents an approach for detecting and locating transformer inter-turn faults using continuous wavelet transform (CWT) and convolutional neural network (CNN). Transformer three-phase voltages and currents are used as input parameter. The input data is processed using the CWT, resulting in six scaleogram images. The six scaleogram images are normalized and concatenated into a single image. The concatenated image is then fed into a trained CNN which indicates whether an inter-turn fault has occurred. Where an inter-turn fault is identified, the magnitude/severity of the fault as well as the affected phase is determined. The technique was tested via the simulation of a three-phase 630kVA, 10.5kV/0.4kV, transformer. Test results show that the approach has remarkable potential for field application.