IDENTIFICATION OF FAULT LOCATION ON TRANSMISSION LINES USING WAVELET THRESHOLD
Keywords:
Wavelet transform, discrete wavelet transform, signal de-noising, transmission line faultsAbstract
Power system fault location and identification of the different faults on a transmission lines for quick & reliable operation of protection scheme. Fault location estimation is very important issue in power system in order to clear faults quickly & restore power supply but the location of fault can be analyzed only with wavelet transform. Wavelet transform, which is very fast and sensitive to noise, is used to extract transients in the line currents for fault detection. The de-noising process rejects noise by thresholding in the wavelet domain and also improves the quality of the signal. Three wavelet functions ("db", "coif" and "sym") and three different thresholding methods are “Rigsure”, “Sqtwolog” and “Minimax” used to de-noise the noise signal. Thresholding rule for three different performance measures were considered to select the appropriate wavelet function to efficient noise removal methods such as, signal-to-noise ratio (SNR), mean square error (MSE), and smoothing ratio, it can be a good way to evaluate the equality of wavelet threshold de-noising. The results show that the wavelet transform can achieve excellent results in signal de-noising; de-noised signal using soft-threshold method is smoother and Soft-threshold method is more suitable.
At the end, I used the classification of wavelet threshold analysis for signal decomposition to monitor some of the faults (e.g. L-G Fault, LL-G Fault, and LLL-G Fault,) in the transmission system. MATLAB simulation results are presented showing the selection of proper threshold value for fault detection also applied the wavelet Toolbox for use with MATLAB for find out the location of the fault.
Keyword: Wavelet transform, discrete wavelet transform, signal de-noising, transmission line faults
References
Choi, M.S., D.S. Lee and X. Yang, 2005. “A line to ground fault location algorithm for underground cable system”, KIEE International Transactions on Power Engineering, 54: 267-273.
IEEE Std. C37.114-2004 “IEEE Guide for Determining Fault Location on AC Transmission and Distribution Lines,” In: Proceedings of IEEE transmission and distribution conference, 8 December 2004.
Raghuveer M. Rao & Ajit S. Bopadiker , “Wavelet Transforms –Introduction to Theory and Applications".
Pearson Education Asia, 1998. Bartušek K., Přinosil J., Smékal Z., Wavelet-based de-noising techniques in MRI, Computer Methods and Programs in Biomedicine, (2011), vol. 104, Issue 3, Pages 480-488.
Donoho, D.L. (1995), "De-noising by soft-thresholding," IEEE Trans. on Inf. Theory, 41, 3, pp. 613-627.
Fletcher A.K., De-noising via Recursive Wavelet Thresholding, Master of Science in Electrical Engineering in the Graduate Division of the University of California, Berkele, (2002).
C. Sidney Bums, Ramesh A. Gopinath, Haitao Guo (1998), “Introduction to Wavelets and Wavelet Transforms”.
M. Karimi, et al (2000), “Wavelet based on-line disturbance detection for power quality applications,” IEEE Trans. on Power Delivery, 01.15, no. 4, pp.1212-1220 OCT’
Shyh-Jier Huang, Cheng-Tao Hsieh and Ching-Lien Huang ,” Application of Wavelets to Classify Power system Disturbances”, Electric Power Systems Research, Volume 47(2), 15 October (1998), 87-93.
C.H. Kim and R. Aggrawal, “Wavelets Transforms in Power Systems”, Power Engineering Journal, Volume15, (2001), 193-202.
Mallat, S. G., A theory of multiresolution signal decomposition: the wavelet representation. IEEE T. Pattern Anal. 1989, 11, 674–693.
Chen S., Zhu H.Y., Wavelet transform for processing power quality disturbances, EURASIP Journal on Advanced in Signal Processing, Vol. 2007, article ID 47695.
Bruce, Andrew, 1996. Applied Wavelet Analysis with S-plus, New York: Springer-Verlag, XXI, 3385: IL.
John stone, I. M., Silverman, B. W., Wavelet threshold estimators for data with correlated noise. J. Roy. Stat. Soc. B 1997, 59, 319–351.
S.A. Chouakri, F. Bereksi-Reguig, S. Ahmaïdi, and O. Fokapu, “Wavelet Denoising of the Electrocardiogram Signal Based on the Corrupted Noise Estimation,” IEEE Computers in Cardiology, vol. 32, pp.1021-1024, 2005.
P.M. Agante and J.P. Marques detect “ECG Noise Filtering Using Wavelets with Soft-thresholding Methods,” IEEE Computers in Cardiology, vol. 26, pp.535-538, 1999
.
F. Nazan Uçar, M. Korürek, and E. Yazgan“A Noise Reduction Algorithm in ECG Signals Using Wavelet Transform,”Proc. IEEE 2nd Int. Biomed. Eng.Day,1997, pp.36-38.
Sunusi. Sani Adamu, Sada Iliya, “Fault Location and Distance estimation on Power Transmission lines using Discrete Wavelet Transform”, International Journal of Advances in Engineering & Technology, Nov 2011.
Downloads
Published
Issue
Section
License
Copyright Notice
Submission of an article implies that the work described has not been published previously (except in the form of an abstract or as part of a published lecture or academic thesis), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher. The Editors reserve the right to edit or otherwise alter all contributions, but authors will receive proofs for approval before publication.
Copyrights for articles published in World Scholars journals are retained by the authors, with first publication rights granted to the journal. The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.
