Matlab Output

on Fault Detection and Classification

This project deals with the application of wavelet based alienation technique for the detection and classification of faults on transmission lines. The three phase current signals of both the ends are synchronized with the help of Global Positioning System clock. These signals are decomposed with Haar wavelet to obtain approximate coefficients over a moving window of half cycle. Approximate Coefficients obtained over a half cycle are compared with the previous half cycle of same polarity to compute alienation coefficients at each end. A Fault Index, computed by adding alienation coefficients of local and remote end, is compared with the threshold to detect and classify the faults. The proposed algorithm has been tested successfully for various types of faults at different fault locations and different fault incidence angles.The stability of power system is largely affected by faults on the transmission line and time required to clear the faults. About two-third of the faults occur on transmission line network. Thus, power system stability and power quality is largely dependent on transmission line protection schemes. Quick detection of faults helps in faster maintenance and restoration of supply resulting in improved economy and reliability of power system. Wavelet Transform (WT) is an effective tool in analyzing transient current signals associated with faults both in frequency and time-domain.Distance protection schemes using WT based phasor estimation was reported.Wavelet based protection scheme for fault detection, classification and location was proposed. protection schemes based on WT had been proposed for series and parallel transmission systems. A protection scheme using wavelet based transient extraction for fault detection for transmission lines was proposed by P. Venugopal Rao et al.For three terminal transmission lines wavelet transform based approach had been proposed, in which first and second peaks of fault generated transients are used for estimating fault location.The performance of the scheme can be improved by using synchronized sampling of signals.Wavelet Transform (WT) is an efficient means of analyzing transient currents and voltages, in both frequency and time-domain. WT not only analyzes the signal in frequency bands but also provides non-uniform division of frequency domain, thus WT uses short window at high frequencies and long window at low frequencies. This helps to analyze the signal in both frequency and time domains effectively. A set of basis functions called Wavelets, are used to decompose the signal in various frequency bands, which are obtained from a mother wavelet by dilation and translation.

Code Title Year
1 Transmission line fault detection and classification IEEE 2019
2 Fault detection and classification in transmission lines based on a PSD index IEEE 2019
3 Transmission line fault detection and classification IEEE 2019
4 A review on Fault Detection, Classification and its Location Evaluation Methodologies in Transmission Lines IEEE 2019
5 Remote monitoring system for real time detection and classification of transmission line faults in a power grid using PMU measurements IEEE 2019
6 Transmission line distance protection using wavelet transform algorithm IEEE 2019
7 A DFT-ED based approach for detection and classification of faults in electric power transmission networks IEEE 2019
8 A realtime fault detection and classification algorithm for transmission line faults based on MODWT during power swing IEEE 2019