Izvorni znanstveni članak
Transformer Faults Classification Based on Convolution Neural Network
Maha A. Elmohallawy
; Department of electrical Engineering, Zagazig Higher Institute of Engineering and Technology, Zagazig, Egypt
Amir Yassin Hassan ; Department of Power Electronics and Energy Conversion, Electronics Research Institute, Cairo, Egypt
Amal F. Abdel-Gawad ; Faculty of computer and informatics, Zagazig University, Zagazig, Egypt.
Sameh I. Selem ; Electrical Power & Machines Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt
This paper studies the latest advances made in Deep Learning (DL) methods utilized for transformer inrush and fault currents classification. Inrush and fault currents at different operating conditions, initial flux and fault type are simulated. This paper presents a technique for the classification of power transformer faults which is based on a DL method called convolutional neural network (CNN) and compares it with traditional artificial neural network (ANN) and other techniques. The inrush and fault current signals of the transformer are simulated within MATLAB by using Fourier analyzers that provides the 2nd harmonic signal. The 2nd harmonic peak and variance statistic values of input signals of the three phases of transformer are used at different operating conditions. The resulted values are aggregated into a dataset to be used as an input for the CNN model, then training and testing the CNN model is performed. Consequently, it is obvious that the CNN algorithm achieves a better performance compared to other algorithms. This study helps with easy discrimination between normal signals and faulty signals and to determine the type of the fault to clear it easily.
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