Impulse Fault Detection and Classification in Power Transformers with Wavelet and Fuzzy Based Technique

Abstract

Impulse testing of transformers after assembly is a routine procedure carried out for the assessment of their winding insulation. During impulse test insulation failure may result in two classes of winding faults in a transformer namely series faults and shunt faults. Series faults are due to the short between turns in the section and the shunt faults are due to the short between turns in the section and the ground. This paper aims at deriving a technique for the detection and classification of impulse faults in a transformer winding using wavelet transform and a fuzzy Inference system. A specially designed 6.6 kV model layer winding is considered for the study. The entire winding comprising ten sections are divided into three regions namely sections near line end, sections near the neutral end and the sections in the middle of the winding. The neutral currents are recorded with series faults and shunt faults introduced in the sections belonging to the three regions. Continuous wavelet transform is applied on these neutral current records to extract the discriminating features. The features extracted from the wavelet transformed signal are the second most predominant frequency, the time range at which it occurs and the corresponding wavelet coefficient. A fuzzy Inference system is designed and implemented using Matlab software with these three features extracted from the wavelet transformed signal as inputs and generates an output that classifies the fault and no fault conditions. It is observed that the results are satisfactory.