Discrete wavelet transform and support vector machine-based parallel transmission line faults classification

IEEJ Transactions on Electrical and Electronic Engineering

Volumn 11, Issue 1, 2016, Pages 43-48

  Saber, Ahmed ^a   Emam, Ahmed ^a   Amer, Rabah ^a  

^a CAIRO UNIVERSITY   (Egypt)

Author keywords

Support vector machine; Transmission line; Wavelet transform

Indexed keywords

CLASSIFICATION (OF INFORMATION); COUPLINGS; DISCRETE WAVELET TRANSFORMS; ELECTRIC LINES; MATLAB; SUPPORT VECTOR MACHINES;

DOUBLE-CIRCUIT PARALLEL TRANSMISSION LINES; FAULT CLASSIFICATION; FAULT RESISTANCES; LOADING CONDITION; MATLAB /SIMULINK; MUTUAL COUPLING; PARALLEL TRANSMISSION LINES; POWER SYSTEM SIMULATIONS;

WAVELET TRANSFORMS;

EID: 84955638992     PISSN: 19314973     EISSN: 19314981     Source Type: Journal    
DOI: 10.1002/tee.22187     Document Type: Article

References (21)

1. Khorashadi-Zadeh H. Artificial neural network approach to fault classification for double circuit transmission lines. Transmission and Distribution Conference and Exposition: Latin America, IEEE/PES, 8-11 November 2004, 2004; 859-862.
2. Thukaram D, Khincha HP, Vijaynarasimha HP. Artificial neural network and support vector machine approach for locating faults in radial distribution systems. IEEE Transactions on Power Delivery 2005; 20:710-721.
3. Razi K, Hagh NT, Abrabian G. High accurate fault classification of power transmission line using fuzzy logic. International Power Engineering Conference (IPEC 2007), 3-6 December 2007, Singapore, 2007; 42-46.
4. Kezunoic M. A survey of neural net application to fault analysis. Engineering Intelligent Systems 1997; 5(4):185-192.
5. Vapnik VN. The Nature of Statistical Learning Theory. Springer Verlag: New York; 1995.
6. Vapnik VN. The Statistical Learning Theory. Wiley, New York; 1998.
7. Salat R, Osowski S. Accurate fault location in the power transmission line using support vector machine approach. IEEE Transactions on Power Systems 2004; 19:879-886.
8. Vapnik V. The support vector method of function estimation. In Nonlinear Modeling: Advanced Black-Box Techniques. Suykens J, Vandewalle J (eds), Kluwer Academic Publishers; AT&T Labs-Research, USA; 1998; 55-86.
9. Ravikumar B, Thukaram D, Khincha HP. Fault diagnosis in power transmission systems using support vector machines. International Journal of Emerging Electric Power Systems 2007; 8(4):1-32.
10. Rosa M, Nelson V. An overview of wavelet transforms application in power systems. 14th PSCC, Sevilla; 2002; 24-28.
11. Misiti M, Misiti Y, Oppenheim G, Poggi JM. Wavelet Toolbox Users Guide. The MathWorks, Natick, Massachusetts, USA, Inc: 2001.
12. Tag Eldin EM. Characterization of power quality disturbances based on wavelet transforms. International Journal of Energy Technology and Policy 2006; 4:74-84.
13. Fikri M, El-Sayed MAH. New algorithm for distance protection of high voltage transmission lines. IEE Proceedings C Generation, Transmission and Distribution 1988; 135:436-440.
14. Sadeh J, Ranjbar AM, Hadjsaid N, Feuillet R, Tran-Quoc T. New method for fault location in power transmission lines using one terminal data. International Conference on Electric Power Engineering, 1999. Power Tech Budapest 99, August 29 1999-September 2 1999, Budapest, Hungary, 1999; p. 254.
15. Yusuff AA, Fei C, Jimoha AA, Munda JL. Fault location in a series compensated transmission line based on wavelet packet decomposition and support vector regression. Electric Power Systems Research 2011; 81(7):1258-1265.
16. Gunn SR. Support Vector Machines for Classification and Regression Technical Report, University of Southampton, 1998.
17. Dash K, Samantaray SR, Ganapati P. Fault classification and section identification of an advanced series-compensated transmission line using support vector machine. IEEE Transactions on Power Delivery 2007; 22:67-73.
18. Truchetet F, Laligant O. Wavelets in industrial applications. SPIE Proceedings 2004; 5607:1-14.
19. Ekici S, Yildirim S, Poyraz M. Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition. Expert Systems with Applications 2008; 34(4):2937-2944.
20. Ekici S, Yildirim S. Fault location estimation on transmission lines using wavelet transform and artificial neural network. Proceedings of the 2006 International Conference on Artificial Intelligence, ICAI 2006, Las Vegas, Nevada, USA, vol. 1, 2006; 181-184.
21. Suykens JAK, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J. Least Squares Support Vector Machines. World Scientific: Singapore; 2002. Available at: http://www.esat.kuleuven.be/sista/lssvmlab/. Accessed July 2012.