Abstract: The demand for a reliable supply of electrical energy for the exigency of modern world has increased considerably requiring nearly a no-fault operation of power systems. Power transformers are very expensive and vital components of electric power systems. The crucial objective to mitigate the frequency and duration of unwanted outages related to power transformer puts a high demand on power transformer protective relays to operate effectively. The high demand includes the requirements of dependability associated with no false tripping, and operating speed with short fault detection and clearing time. The second harmonic restrain principle is widely used in industrial application for many years, which uses Discrete Fourier Transform (DFT). This principle often encounters some problems such as long restrain time and inability to discriminate internal fault from magnetizing inrush condition. Hence, Artificial Neural Network (ANN), which has the ability to mimic and automate knowledge, has been proposed for detection and classification of faults in this paper. The Wavelet Transforms (WT) which has the ability to extract information from transient signals in both time and frequency domain simultaneously is used for the analysis of power transformer transient phenomena. The conditions of power transformer to be analysed in a power system are modelled in MATLAB/SIMULINK environment and implemented using LabVIEW software.

Keywords: Power Transformer, Artificial Neural Network, Wavelet Transform, Current Transformer, LabVIEW

| DOI: 10.17148/IJIREEICE.2019.7807