Fault location estimation on transmission lines using wavelet transform and artificial neural network

Abstract

This paper presents a wavelet transform (WT) and artificial neural network (ANN) based algorithm for estimating fault location on transmission lines. The algorithm is developed as a one-end frequency based technique and used both voltage and current effect resulting from remote end of the power system. Fault simulation is carry out in Alternative Transient Program (ATP). One cycle of waveform, covering pre-fault and post-fault information is abstracted for analysis. The discrete wavelet transform (DWT) is used for data preprocessing and this data are used for training and testing ANN. Five types of mother wavelet are used for signal processing to identify a suitable wavelet family that is more appropriate for use in estimating fault location. It is found that the proposed method gives satisfactory results and will be useful for estimating fault location.