Short-Circuit Damage Diagnosis in Transformer Windings Using Quaternions: Severity Assessment through Current and Vibration Signals

Abstract

Short circuits occurring between turns within the windings are widely known as one of the primary causes of damage in electrical transformers; as a result, early detection plays a fundamental role in preventing further and more serious damage. This study introduces a novel approach that relies on the analysis of current and vibration signals, specifically employing the analysis of quaternion signals, to effectively detect short circuits within electrical transformers., offering an identification of conditions ranging from a healthy state to six levels of short circuit turns. in a no-load transformer, i.e., 0, 5, 10, 15, 20, 25 and 30 SCT. This proposed method employs quaternion rotation to extract statistical features that can be used to classify the condition of the transformer. To evaluate the effectiveness of the proposed methodology, an experimental validation is carried out using a 1.5 kVA transformer, comparing its performance against other existing methods. The results demonstrate the feasibility of the proposal, accurately identifying various levels of SCT, achieving an accuracy of 97.5%, using only 100 samples with the k nearest neighbors method.