A New Method for Fault Detection and Identification of Incipient Faults in Power Transformers

Abstract

This article presents a new scheme for incipient fault detection and its identification in transformers. The new approach is actually based on adaptive modeling of transformers using the transmission line method (TLM) obtained from the hysteresis model. The adaptive TLM observer representing no-load, quarter-load, half-load, and rated-load conditions is used for faults detection. The continuous wavelet transform (CWT) is performed on residuals that are obtained by comparing real system currents and calculated TLM observer currents in order to extract the features for fault identification. An adaptive fuzzy reasoning technique is used to identify incipient faults in the transformer. The sum of CWT coefficients of residuals is applied to the adaptive fuzzy rule-based decision-making unit to indicate the type of faults. The main advantage of the suggested scheme is that different types of incipient faults in the transformer can be correctly identified. The test results verify the effectiveness of the suggested method.