Detection and Classification of Lamination Faults in a 15 kVA Three-Phase Transformer Core Using SVM, KNN and DT Algorithms
Ehsan Altayef, Fatih Anayi, Michael Packianather, Youcef Benmahamed, Omar Kherif
Abstract
This paper deals with the detection and classification of two types of lamination faults (i.e., edge burr and lamination insulation faults) in a three-phase transformer core. Previous experimental results are exploited, which are obtained by employing a 15 kVA transformer under healthy and faulty conditions. Different test conditions are considered such as the flux density, number of the affected laminations and fault location. Indeed, the current signals are used where four features (Average, Fundamental, THD and STD) are extracted. Elaborating A total of 328 samples, these features are utilized as input vectors to train and test classification models based on SVM, KNN and DT algorithms. Based on the selected features, the results confirm that the transformer current can be used for the detection of lamination faults. An accuracy rate of more than 84% is obtained using three different classifiers. Such findings provide a promising step toward fault detection and classification in electrical transformers, helping to prevent the system and avoid other related issues such as the increase of power loss and temperature.