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Wavelet Transform Technique For Fault Detection On Power System Transmission Line
Subject area: Science,Engineering and Technology · Area of research: Electrical Engineering
Abstract
This paper presents a discrete wavelet transform and neural network approach to fault detection and classification in transmission line faults. The detection is carried out by the analysis of the details coefficients energy of the phase signals, and as an input to neural network to classify the faults on transmission lines. Neural network perform well when faced with different fault conditions and system parameters. In this paper, WT has been applied to the output phase A unbalance fault voltage and current signals of a typical transmission line modeled with MATLAB/SIMULINK 2016. The Phase A were simulated on the line and their pre ? fault and fault voltage and current per ? unit output values were generated and produced waveforms of pre ? fault and fault signals. The results of the MRA fault detection analysis show that the wavelet transform method is more accurate in detecting the various faults of a transmission line than any other signal analysis techniques.
Keywords
Wavelet Transform, Fault detection, Transmission line, Multi resolution analysis, Transient energy
References
[1] Reddy B. R., 2009, ‘Detection and Location of Faults on Transmission Line Using Coiflet Mother Wavelet Transform’, Journal of Theoretical and Applied Information Technology. 2005 – 2009. www.jatit.org
[2] Chengzong P., Mladenk, Zhang N., 2010 ‘Wavelet Based Method for Transmission Line Fault Detection and Classification During Power Swing’, Texas A & M University College Station, TX77843, USA, WERC 323, MS 3128
[3] Matlab/Simulink 2016
[4] Karthikeyan J., 2007., ‘Power System Fault Detection and Classification by Wavelet Adapting Resonance Theory Neural Network’, University of Kentucky, www.uknowledge.uky.edu/gradschool_the sis/452
[5] Patel M., and Patel R. N., (2012), ‘ Fault Detection and Classification on a Transmission Line using Wavelet Multi Resolution Analysis and Neural Network’, International Journal of Computer Applications (0975 – 8887) Volume 47– No.22, June 2012.
[6] Ngu E. E., Ramar K., Montano R., and Cooray V., (2008), ‘A Study on Different Fault Characteristics Using Wavelet and Fast Fourier Transforms’, 7th WSEAS International Conference on Application of Electrical Engineering (AEE’08), Trondheim, Norway, July 2-4, 2008.
How to cite this paper
@article{1701611,
author = {V. C. OGBOH , C. P. EZEAKUDO, E. C. NWANGUGU},
title = {Wavelet Transform Technique For Fault Detection On Power System Transmission Line},
journal = {Iconic Research And Engineering Journals},
year = {2019},
volume = {3},
number = {3},
pages = {47-52},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1701611.pdf},
abstract = {This paper presents a discrete wavelet transform and neural network approach to fault detection and classification in transmission line faults. The detection is carried out by the analysis of the details coefficients energy of the phase signals, and as an input to neural network to classify the faults on transmission lines. Neural network perform well when faced with different fault conditions and system parameters. In this paper, WT has been applied to the output phase A unbalance fault voltage and current signals of a typical transmission line modeled with MATLAB/SIMULINK 2016. The Phase A were simulated on the line and their pre ? fault and fault voltage and current per ? unit output values were generated and produced waveforms of pre ? fault and fault signals. The results of the MRA fault detection analysis show that the wavelet transform method is more accurate in detecting the various faults of a transmission line than any other signal analysis techniques.},
keywords = {Wavelet Transform, Fault detection, Transmission line, Multi resolution analysis, Transient energy},
month = {September},
}