REVIEW ON FAULT DETECTION USING ANN AND WAVELET TRANSFORM FOR POWER TRANSFORMER

Abstract

The need for a reliable supply of electrical energy for the requirements of the modern world in all fields has increased significantly, requiring the fault less operation of electrical systems. The overriding objective is to minimize the frequency and duration of unexpected failures associated with power transformers with peak demand, including reliability requirements related to zero bias and operating speed with the ability to detect and eliminate errors in a short time. The second harmonic restrain principle has been used in industrial applications for many years using the Discrete Fourier Transform (DFT) and often encounters the problems unable to identify magnetizing inrush state internal fault and longer restrain time. Therefore, artificial neural network (ANN), a powerful tool of artificial intelligence (AI), capable of mimicking and automating knowledge, has been proposed to detect and analyze types of defects under normal and fault conditions.