Classification and Discrimination Among Winding Mechanical Defects, Internal and External Electrical Faults, and Inrush Current of Transformer

In this paper, the mechanical faults of transformers including the winding radial deformation and axial displacement on 1.6 MVA transformer winding are investigated. Then, by estimating the parameters of the detailed model of this transformer winding in MATLAB software and changing these parameters in a manner that is proportional to the mechanical defects in electro-magnetic transients program software, the sampled differential current of the transformer is extracted for each disturbance. Next, the internal and external electrical faults and inrush current of the transformer are simulated. Afterwards, these signals are analyzed using maximal overlap discrete wavelet transform with Daubechies4 wavelet function, and their features are extracted. These extracted features are considered for training the classifiers of Decision Tree and artificial neural network. According to the simulation results, the proposed procedure is capable of classifying and discriminating among winding mechanical defects, internal and external electrical faults, and inrush current with a good accuracy that is the main novelty of this paper in comparison to other published works, which are limited to classifying only some of the mentioned faults.