Early Detection of Turn-to-Turn Faults in Power Transformer Winding: An Experimental Study

Abstract

Turn-to-turn insulations of transformer windings may degrade gradually because of mechanical forces, thermal stresses or chemical corrosion. Degradation decreases impedances of inter-turn insulations that finally may lead to a solid turn-to-turn short circuit. In this paper, early detection of turn-to-turn faults in transformers windings has been studied, in its high-impedance stage, using Artificial Neural Networks (ANN) based on its Frequency Response (FR). For this purpose, a model winding has been used as test object to approve capability of the proposed approach. A variety of low impedance and high impedance short circuit faults were tested on the model winding. Then the frequency response of winding in both intact and defected conditions is measured using Low Voltage Impulse (LVI) test. A mapping between frequency response and exact location of each fault was made using multi-layer perceptron (MLP) neural network. Extracted features from frequency responses are used to train and test the proposed MLP. The results show that this method is able to detect turn-to-turn faults in transformer winding even in their early stages.