IJSTR

International Journal of Scientific & Technology Research

IJSTR@Facebook IJSTR@Twitter IJSTR@Linkedin
Home About Us Scope Editorial Board Blog/Latest News Contact Us
CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT
QR CODE
IJSTR-QR Code

IJSTR >> Volume 4 - Issue 6, June 2015 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Most Suited Mother Wavelet For Localization Of Transmission Line Faults

[Full Text]

 

AUTHOR(S)

Sushma Verma

 

KEYWORDS

Keywords: Transmission line, Fault localization, Discrete Wavelet Transform (DWT), Mother wavelet, Wavelet function, Artificial Neural Network (ANN), Alternate Transient Program (ATP).

 

ABSTRACT

Abstract: This paper is a modest approach to determine the most suited mother wavelet for localization of transmission line faults. Discrete wavelet transform (DWT) and artificial neural network (ANN) based algorithm has been developed for this purpose. Extensive simulation studies were carried out in ATP for various types of fault conditions, locations and fault resistances. DWT analysis of the sending end current signals was done using ‘daubechies’ wavelets. Five wavelets: ‘db1’, ‘db2’, ‘db3’, ‘db4’ & ‘db5’ were selected associated with different centre frequency and period. The statistical features extracted from the DWT coefficients of the sending end current signals were used to train the ANN for identifying the fault locations. The results shows that the ‘db3’ mother wavelet is best suited for localization of transmission line faults, because of its short period and more number of vanishing moments.

 

REFERENCES

[1] I.Daubechies, “Ten lectures on wavelets,” Society for industrial and applied mathematics, Philadelphia, PA, 1992.

[2] T.B.Littler &D.J Morrow, “Wavelets for the analysis and compression of power system disturbances,” IEEE Transactions on power delivery, vol4, pp358-64, apr1999

[3] Zwee-Lee Giang, “Wavelet based neural network for power disturbance recognition and classification,” IEEE Transactions on power delivery, vol19, pp1560-68, oct2004.

[4] P. Pillay & A. Bhattacharjee, “Application of wavelets to model short term power system disturbances,” IEEE Transactions on power systems, vol1, pp2031-37, nov1996.

[5] S. Santoso, E.J Powers & P.Hofmann, “Power quality assessment via wavelet transform Analysis,”IEEE Transactions on power deliverys , vol11,issue 2,, pp3387-97, 1981

[6] O.A.S.Youssef, “Fault classification based on wavelet transforms,” Transmission and Distribution Conference and Exposition, 2001 IEEE/PES

[7] M.Y Chow, S.O Yee &L.S Taylor, “Recognizing animal-caused faults in power distribution Systems using ANN,” IEEE Transactions on power delivery, vol. 8, pp1268-74, 1993.

[8] Robertson DC, Camps OI, Mayer JS, “Wavelet and electromagnetic power system transients,” IEEE Transactions on power delivery, vol11, issue2, pp1050-58, 1996.

[9] Reena Sharma, Aziz Ahmad, Shailendra Kr. Saroj, “Protection of Transmission Lines using Discrete Wavelet Transform”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-3, Issue-1, June 2013 .

[10] MukeshThakre, Suresh Kumar Gawre & Mrityunjay Kumar Mishra, “Distribution System faults Classification And Location Based On Wavelet Transform”, International Journal on Advanced Computer Theory and Engineering (IJACTE) ISSN (Print) : 2319 – 2526, Volume-2, Issue-4, 2013.

[11] Ngu Eng Eng, Krishnathevar Rama;“Single-Ended Traveling Wave Based Fault Location on Two Terminal Transmission Lines”,IEEE2009

[12] Tahar Boothbay, “Fault Location In EHV Transmission Lines Using Artificial Neural Networks”, International Journal of Applied Mathematics and Computer Science, Vol. 14, 2004

[13] M.E.Baran,J.Kim, “A classifier for Distribution Feeder Overcurrent Analysis,” IEEE Transactions on power delivery, vol21,No1, pp456-462, Jan2006.

[14] S.Santoso, W.M.Grady, E.J.Powers, J.Lamoree and S.C.Bhatt, “Characterization of Distribution Power Quality Events with Fourier and Wavelet Transforms, ”, ” IEEE Transactions on power delivery, vol15,No1, pp247-254, Jan-2000.

[15] A.Mgouda, ,M.M.A Salama, M.R Sultan and A.Y.Chikhani, “Power Quality Detection and Classification using Wavelet –Multiresolution Signal Decomposition,” IEEE Transactions on power delivery, vol14,No4, pp1469-76, oct1999.

[16] D. V. Coury D. C. Jorge, “Artificial Neural Network Approach to Distance Protection of transmission lines,” IEEE Transactions on power delivery, vol13,No1, pp102-108, Jan 1998.

[17] Wael R. Anis Ibrahim and Medhat M. Morcos, “Artificial Intelligence and Advanced Mathematical Tools for Power Quality Applications: A Survey”, IEEE Transactions on power delivery, vol. 17, No. 2,pp668-673 April 2002.

[18] A.W.Galli and O.M.Nielse “CAP tuorial:Wavelet analysis for power system transients,” IEEE Comput .Appl.Power, vol12,no1,pp16-16,Jan 1999.

[19] S. Santoso, E.J Powers , W.M.Grady & Antony C. Parsons, “ Power Quality Disturbance Waveform Recognition Using Wavelet-Based Neural Classifier—Part 1: IEEE Transactions on power delivery, vol15No1, pp222-228, Jan-2000

[20] P. Konar, P. Chattopadhyay, “Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs),”Applied Soft Computing, 11 (2011), pp. 4203–4211.

[21] S. Mezghani, L. Sabri, M. El Mansori, H. Zahouani, “An the optimal choice of wavelet function for multi-scale honed surface characterization,”13th International Conference on Metrology and Properties of Engineering Surfaces (2011) pp1-7.

[22] N. D. Kelley, R. Osgood, J. Bialasiewicz, A. Jakubowski, “Using Time-Frequency and Wavelet Analysis to Assess Turbulence/Rotor Interactions,” Proceedings of 19thAmerican Society of Mechanical Engineers (ASME) Wind Energy Symposium, 2000,pp130–149.

[23] R..J..Merry, Wavelet theory and applications- A Literature study, Eindhoven University of technology,(2005),Available online:http://alexandria.tue.nl/repository/books/612762.pdf

[24] Prochazka, J. Uhlir, P. J. W. Payner, N. G. Kingsbury, Signal Analysis and Prediction (Applied and Numerical Harmonic Analysis), Birkhäuser boston (1998).

[25] P.L Mao and R.K.Aggarwal, “A novel approach to the classification of the transient phenomena in power transformers using combined wavelet transform and neural network,” IEEE Transactions on power delivery, vol16,No4,pp654-660,oct 2001.

[26] M.Singh, B.K.Panigrahi and R.P Maheshwari, “Transmission Line Fault Detection and Classification,” Proceedings of ICETECT 2011,pp 15-21.

[27] J.Upendra, C.P Gupta and G.K Singh, “Discrete Wavelet Transform and Probablistic Neural Network Based Algorithm for Classification of Fault on Transmission system”, IEEE Transactions on power delivery , 2008.

[28] P.S Bhowmick, P.Purkait and K.Bhattacharya, “A Novel Wavelet Transform And Neural network Based Transmission Line Fault Analysis Method”, Developments in Power System Protection, 2008. DPSP 2008. IET 9th International Conference, 2008, pp477-483.

[29] S.Verma, P. Konar and Dr.P.Chattopadhyay, “A Wavelet Based Fult Localisation in Transmission Network”,IEEE conference 28-30 dec,2011.