Skip to main content
Log in

Traveling-Wave Fault Location Techniques in Power System Based on Wavelet Analysis and Neural Network Using GPS Timing

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In this paper four fault location algorithms based on discrete wavelet transform using global positioning system are described and compared. In two approaches, the location of fault is determined according to arrival instances of traveling waves and in two other approaches, the non-linear relations are simulated by artificial neural network to improve the responses. All the possible fault types are generated using the ATP–EMTP and results using the four methods are discussed. Extensive simulation studies indicate that proposed networks decrease errors percentages of two wavelet-based approaches from 0.35 to 0.22 and 0.21 to less than 0.15 %, respectively, though exploiting small size data base for training.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Mora-Florez, J., Melendez, J., & Caicedo, G. C. (2008). Comparison of impedance based fault location methods for power distribution systems. Journal of Electric Power Systems Research, 78(4), 657–666.

    Article  Google Scholar 

  2. Sachdev, M. S., & Agarwal, R. (1988). A technique for estimating transmission line fault locations from digital impedance relay measurements. IEEE Transactions on Power Delivery, 3(1), 121–129.

    Article  Google Scholar 

  3. Izykowski, J., Rosolowski, E., & Saha, M. M. (2007). Postfault analysis of operation of distance protective relays of power transmission lines. IEEE Transactions on Power Delivery, 22(1), 74–81.

    Article  Google Scholar 

  4. Srinivasan, K., & St.-Jacques, A. (1989). A new fault location algorithm for radial transmission lines with loads. IEEE Transactions on Power Delivery, 4(3), 1676–1682.

    Article  Google Scholar 

  5. Girgis, A. A., Hart, D. G., & Peterson, W. L. (1992). A new fault location technique for two-and three-terminal lines. IEEE Transactions on Power Delivery, 7(1), 98–107.

    Article  Google Scholar 

  6. El-Hami, M., Lai, L. L., Daruvala, D. J., & Johns, A. T. (1992). A new traveling-wave based scheme for fault detection on overhead power distribution feeders. IEEE Transactions on Power Delivery, 7(4), 1825–1833.

    Article  Google Scholar 

  7. Christopoulos, C., Thomas, D. W. P., & Wright, A. (1988). Scheme based on traveling waves for the protection of major transmission lines. IEEE Proceedings C (Generation, Transmission and Distribution), 135(1), 63–73.

    Article  Google Scholar 

  8. Jie, L., Elangovan, S., & Devotta, X. (1999). Adaptive traveling wave protection algorithm using two correlation functions. IEEE Transactions on Power Delivery, 14(1), 126–131.

    Google Scholar 

  9. Shehab-Eldin, E. H., & Mclaren, P. G. (1988). Traveling wave distance protection: problem areas and solutions. IEEE Transactions on Power Delivery, 3(3), 894–902.

    Article  Google Scholar 

  10. Spoor, D., & Zhu, J. G. (2006). Improved single-ended traveling-wave fault-location algorithm based on experience with conventional substation transducers. IEEE Transactions on Power Delivery, 21(3), 1714–1720.

    Article  Google Scholar 

  11. Xu, H. H., Hui, Z. B., & Lai, L. Z. (2003). A novel principle of single-ended fault location technique for EHV transmission lines. IEEE Transactions on Power Delivery, 18(4), 1147–1151.

    Article  Google Scholar 

  12. Kezunovic, M., & Perunieic, B. (1996). Automated transmission line fault analysis using synchronized sampling at two ends. IEEE Transactions on Power Delivery, 11(1), 441–447.

    Article  Google Scholar 

  13. Mosavi, M. R. (2011). Error reduction for GPS accurate timing in power systems using kalman filters and neural networks. Journal of Electrical Review, 87(12a), 161–168.

    Google Scholar 

  14. Mosavi, M. R., Nabavi, H., & Nakhaei, A. (2013). Neural technologies for precise timing in electric power systems with a single-frequency GPS receiver. Journal of Wireless Personal Communications,. doi:10.1007/s11277-013-1398-z.

    Google Scholar 

  15. Mosavi, M. R. (2011). Wavelet neural network for corrections prediction in single-frequency GPS users. Neural Processing Letters, 33(2), 137–150.

    Article  Google Scholar 

  16. Jafarian, P., & Sanaye-Pasand, M. (2010). A traveling-wave-based protection technique using wavelet/PCA analysis. IEEE Transactions on Power Delivery, 25(2), 588–599.

    Article  Google Scholar 

  17. Borghetti, A., Bosetti, M., Nucci, C. A., Paolone, M., & Abur, A. (2010). Integrated use of time–frequency wavelet decompositions for fault location in distribution networks: Theory and experimental validation. IEEE Transactions on Power Delivery, 25(4), 3139–3146.

    Article  Google Scholar 

  18. Tabatabaei, A., Mosavi, M. R., & Rahmati, A. (2012). Fault location techniques in power system based on traveling wave using wavelet analysis and GPS timing. Journal of Electrical Review, 88(6), 347–350.

    Google Scholar 

  19. Tawfik, M., & Morcos, M. (2001). ANN-based techniques for estimating fault location on transmission lines using Prony method. IEEE Transactions on Power Delivery, 16(2), 219–224.

    Article  Google Scholar 

  20. Mazon, A. J., Zamora, I., Gracia, J., Sagastabeutia, K. J., & Saenz, J. R. (2001). Selecting ANN structures to find transmission faults. IEEE Transactions on Computer Applications in Power, 14(3), 44–48.

    Article  Google Scholar 

  21. Gracia, J., Mazón, A. J., & Zamora, I. (2005). Best ANN structures for fault location in single and double-circuit transmission lines. IEEE Transactions on Power Delivery, 20(4), 2389–2395.

    Article  Google Scholar 

  22. Mirzaei, M., Ab Kadir, M. Z. A., Moazami, E., & Hizam, H. (2009). Review of fault location methods for distribution power system. Australian Journal of Basic and Applied Sciences, 3(3), 2670–2676.

    Google Scholar 

  23. Thammart, C., Nawikavatan, A., Niyomsat, T. & Bunjongjit, S. (2009) ANN-based technique for fault-location on transmission lines with ATP/EMTP program. In: IEEE international conference on advances in power system control, operation and management, pp. 1–6.

  24. Jain, A., Thoke, A. S., & Patel, R. N. (2009) Double circuit transmission line fault distance location using artificial neural network. In: IEEE world congress on nature & biologically inspired computing, pp. 13–18.

  25. Tabatabaei, A., Mosavi, M. R. & Farajiparvar, P. (2012). A traveling-wave fault location technique for three-terminal lines based on wavelet analysis and recurrent neural network using GPS timing. In: Conference on smart electrical grids technology (SEGT2012), Iran University of Science and Technology, pp. 154–158.

  26. Mosavi, M. R., & Tabatabaei, A. (2014). Wavelet and neural network based fault location in power systems using statistical analysis of traveling wave. The Arabian Journal for Science and Engineering, 39(8), 6207–6214.

    Article  Google Scholar 

  27. Prikler, L., & Holdalen, H. K. (1998) ATP draw for windows 3.1/95/NT Version 1.0 User’s Manual Release 1.0.1.

  28. Addison, P. S. (2002). The illustrated wavelet transform handbook: Introductory theory and applications in science, engineering, medicine and finance. London: Institute of Physics Publishing.

    Book  MATH  Google Scholar 

  29. Brito, N. S. D., Souza, B. A., & Pires, F. A. C. (1998). Daubechies wavelets in quality of electrical power. IEEE Conference on Harmonics and Quality of Power, 1, 511–515.

    Google Scholar 

  30. Mosavi, M. R. (2006). A practical approach for accurate positioning with L1 GPS receivers using neural networks. Journal of Intelligent & Fuzzy Systems, 17(2), 159–171.

    Google Scholar 

  31. Mosavi, M. R. (2007). GPS receivers timing data processing using neural networks: Optimal estimation and errors modeling. Journal of Neural Systems, 17(5), 383–393.

    Article  Google Scholar 

  32. Ku, C. C., & Lee, K. Y. (1992). Nonlinear system identification using diagonal recurrent neural networks. IEEE Conference on Neural Networks, 3, 839–844.

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank Iran Distribution Management Company, for their valuable support during the authors’ research work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohammad-Reza Mosavi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mosavi, MR., Tabatabaei, A. Traveling-Wave Fault Location Techniques in Power System Based on Wavelet Analysis and Neural Network Using GPS Timing. Wireless Pers Commun 86, 835–850 (2016). https://doi.org/10.1007/s11277-015-2958-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-015-2958-1

Keywords

Navigation