Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 823720, 9 pages
http://dx.doi.org/10.1155/2015/823720
Research Article

Locating High-Impedance Fault Section in Electric Power Systems Using Wavelet Transform, -Means, Genetic Algorithms, and Support Vector Machine

Department of Electrical Engineering, Chung Yuan Christian University, 200 Chung Pei Road, Chung Li 320, Taiwan

Received 22 July 2014; Accepted 14 November 2014

Academic Editor: Vishal Bhatnaga

Copyright © 2015 Ying-Yi Hong and Wei-Shun Huang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. K. Y. Lien, S. L. Chen, C. J. Liao, T. Y. Guo, T. M. Lin, and J. S. Shen, “Energy variance criterion and threshold tuning scheme for high impedance fault detection,” IEEE Transactions on Power Delivery, vol. 14, no. 3, pp. 810–817, 1999. View at Publisher · View at Google Scholar · View at Scopus
  2. A. E. Emanuel, D. Cyganski, J. A. Orr, S. Shiller, and E. M. Gulachenski, “High impedance fault arcing on sandy soil in 15kV distribution feeders: contributions to the evaluation of the low frequency spectrum,” IEEE Transactions on Power Delivery, vol. 5, no. 2, pp. 676–686, 1990. View at Publisher · View at Google Scholar · View at Scopus
  3. C. H. Kim, H. Kim, Y. H. Ko, S. H. Byun, R. K. Aggarwal, and A. T. Johns, “A novel fault-detection technique of high-impedance arcing faults in transmission lines using the wavelet transform,” IEEE Transactions on Power Delivery, vol. 17, no. 4, pp. 921–929, 2002. View at Publisher · View at Google Scholar · View at Scopus
  4. A.-R. Sedighi, M.-R. Haghifam, O. P. Malik, and M.-H. Ghassemian, “High impedance fault detection based on wavelet transform and statistical pattern recognition,” IEEE Transactions on Power Delivery, vol. 20, no. 4, pp. 2414–2421, 2005. View at Publisher · View at Google Scholar · View at Scopus
  5. T. M. Lai, L. A. Snider, E. Lo, and D. Sutanto, “High-impedance fault detection using discrete wavelet transform and frequency range and RMS conversion,” IEEE Transactions on Power Delivery, vol. 20, no. 1, pp. 397–407, 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Michalik, W. Rebizant, M. R. Lukowicz, S. J. Lee, and S. H. Kang, “High-impedance fault detection in distribution networks with use of wavelet-based algorithm,” IEEE Transactions on Power Delivery, vol. 21, no. 4, pp. 1793–1802, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Sheng and S. M. Rovnyak, “Decision tree-based methodology for high impedance fault detection,” IEEE Transactions on Power Delivery, vol. 19, no. 2, pp. 533–536, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. C. S. Burrus, R. A. Gopinath, and H. Guo, Introduction to Wavelets and Wavelet Transforms, Prentice Hall, Upper Saddle River, NJ, USA, 1998.
  9. A. Elmitwally, S. Farghal, S. Abdelkader, and M. Elkateb, “Proposed wavelet-neurofuzzy combined system for power quality violations detection and diagnosis,” IEE Proceedings: Generation, Transmission and Distribution, vol. 148, no. 1, pp. 15–20, 2001. View at Publisher · View at Google Scholar · View at Scopus
  10. L. Angrisani, P. Daponte, M. D'Apuzzo, and A. Testa, “A measurement method based on the wavelet transform for power quality analysis,” IEEE Transactions on Power Delivery, vol. 13, no. 4, pp. 990–998, 1998. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Santoso, E. J. Powers, W. M. Grady, and A. C. Parsons, “Power quality disturbance waveform recognition using wavelet-based neural classifier. Part 1. theoretical foundation,” IEEE Transactions on Power Delivery, vol. 15, no. 1, pp. 222–228, 2000. View at Publisher · View at Google Scholar · View at Scopus
  12. H. Mokhtari, M. Karimi-Ghartemani, and M. R. Iravani, “Experimental performance evaluation of a wavelet-based on-line voltage detection method for power quality applications,” IEEE Transactions on Power Delivery, vol. 17, no. 1, pp. 161–172, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. P. K. Dash, S. R. Samantaray, and G. Panda, “Fault classification and section identification of an advanced series-compensated transmission line using support vector machine,” IEEE Transactions on Power Delivery, vol. 22, no. 1, pp. 67–73, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. D. Srinivasan, W. S. Ng, and A. C. Liew, “Neural-network-based signature recognition for harmonic source identification,” IEEE Transactions on Power Delivery, vol. 21, no. 1, pp. 398–405, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. P. Janik and T. Lobos, “Automated classification of power-quality disturbances using SVM and RBF networks,” IEEE Transactions on Power Delivery, vol. 21, no. 3, pp. 1663–1669, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. S. Fan and L. Chen, “Short-term load forecasting based on an adaptive hybrid method,” IEEE Transactions on Power Systems, vol. 21, no. 1, pp. 392–401, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. L. S. Moulin, A. P. Alves Da Silva, M. A. El-Sharkawi, and R. J. Marks II, “Support vector machines for transient stability analysis of large-scale power systems,” IEEE Transactions on Power Systems, vol. 19, no. 2, pp. 818–825, 2004. View at Publisher · View at Google Scholar · View at Scopus
  18. N. K. Bose and P. Liang, Neural Network Fundamentals with Graphs, Algorithms, and Applications, McGraw-Hill, New York, NY, USA, 1996.
  19. Y. K. Lam and P. W. M. Tsang, “eXploratory K-Means: a new simple and efficient algorithm for gene clustering,” Applied Soft Computing Journal, vol. 12, no. 3, pp. 1149–1157, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. T. Velmurugan, “Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data,” Applied Soft Computing Journal, vol. 19, pp. 134–146, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: the fuzzy c-means clustering algorithm,” Computers & Geosciences, vol. 10, no. 2-3, pp. 191–203, 1984. View at Publisher · View at Google Scholar · View at Scopus
  22. D. Datta, “Unit commitment problem with ramp rate constraint using a binary-real-coded genetic algorithm,” Applied Soft Computing Journal, vol. 13, no. 9, pp. 3873–3883, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Jamil, A. Kalam, A. Q. Ansari, and M. Rizwan, “Generalized neural network and wavelet transform based approach for fault location estimation of a transmission line,” Applied Soft Computing, vol. 19, pp. 322–332, 2014. View at Publisher · View at Google Scholar · View at Scopus
  24. D. Bayram and S. Şeker, “Wavelet based Neuro-Detector for low frequencies of vibration signals in electric motors,” Applied Soft Computing Journal, vol. 13, no. 5, pp. 2683–2691, 2013. View at Publisher · View at Google Scholar · View at Scopus
  25. H. Eristi, “Fault diagnosis system for series compensated transmission line based on wavelet transform and adaptive neuro-fuzzy inference system,” Measurement, vol. 46, no. 1, pp. 393–401, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. S. Ekici, “Support Vector Machines for classification and locating faults on transmission lines,” Applied Soft Computing Journal, vol. 12, no. 6, pp. 1650–1658, 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. M. Matsumoto and J. Hori, “Classification of silent speech using support vector machine and relevance vector machine,” Applied Soft Computing Journal, vol. 20, pp. 95–102, 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. K. S. Chua, “Efficient computations for large least square support vector machine classifiers,” Pattern Recognition Letters, vol. 24, no. 1–3, pp. 75–80, 2003. View at Publisher · View at Google Scholar · View at Scopus
  29. W. M. Grady, M. J. Samotyj, and A. H. Noyola, “Minimizing network harmonic voltage distortion with an active power line conditioner,” IEEE Transactions on Power Delivery, vol. 6, no. 4, pp. 1690–1697, 1991. View at Publisher · View at Google Scholar · View at Scopus