Tunable Q-Factor Wavelet Transform for Classifying Mechanical Deformations in Power Transformer

Mechanical deformations in the power transformer are the result of short circuit forces and improper handling of transformer during transportation. Such deformations grow with the time and might lead to complete breakdown of the transformer. Hence, monitoring the condition of the transformer is essential. This paper presents a technique to analyse the terminal behaviour of the transformer winding. To this end, high frequency circuit model of the transformer winding comprises of inductances, capacitances and resistances is considered initially. Mechanical deformations are then introduced by changing these circuit parameters. Frequency response analysis (FRA) is performed to obtain terminal behavior of the circuit model under both healthy and unhealthy conditions. Signals obtained from FRA are decomposed into five subbands (SBs) using tunable Q-Factor wavelet transform (TQWT). Afterwards, with the help of Shannon Entropy (SE), features of the SBs are extracted. These features are then classified using K-nearest neighbour (KNN) and Ensemble Bagged algorithm (EB). The statistical parameters like p-value and t-test clearly indicated that the signals are classified in to healthy and faulty states. Further, data have been classified properly with an accuracy of 99.2%.