M.T. Yang and J.C. Gu


  1. [1] C.L. Benner & B.D. Russell, Practical high impedance fault detection for distribution feeders, Proc. 39th Annual Conf. on Rural Electric Power, Fort Worth, Texas, 1996, B2-1–B2-6.
  2. [2] A.F. Sultan, G.W. Swift, & D.J. Fedirchuk, Detecting arcing downed-wires using fault current flicker and half-cycle asymmetry, IEEE Transactions on Power Delivery, 9 (1), 1994, 461–470. doi:10.1109/61.277718
  3. [3] D.C.T. Wai & X. Yibin, A novel technique for high impedance fault identification, IEEE Transactions on Power Delivery, 13 (3), 1998, 738–744. doi:10.1109/61.686968
  4. [4] A.M. Sharaf & S.I. Abu-Azab, A smart relaying scheme for high impedance faults in distribution any utilization network, Proc. Canadian Conf. on Electrical and Computer Engineering, Halifax, Nova Scotia, 2000, 740–744.
  5. [5] S. Santoso & P. Hofmann, Power quality assessment via wavelet transform analysis, IEEE Transactions on Power Deliver, 11 (2), 1996, 924–930. doi:10.1109/61.489353
  6. [6] Omar A.S. Youssef, A wavelet-based technique for discrimination between faults and magnetizing inrush currents in transformers, IEEE Transactions on Power Delivery, 18 (1), 2003, 170–176.
  7. [7] J. Liang, S. Elangovan, & J.B.X. Devotta, A wavelet multiresolution analysis approach to fault detection and classification in transmission lines, Electrical Power & Energy Systems, 20 (5), 1998, 327–332. doi:10.1016/S0142-0615(97)00076-8
  8. [8] A. Lazkano, J. Ruiz, E. Aramendi, & L.A. Leturiondo, Anew approach to high impedance fault detection using wavelet packet analysis, Proc. 9th IEEE Int. Conf. on Harmonics and Quality of Power, Orlando, Florida, 2000, 1005–1010.
  9. [9] E.A. Mohamed & N.D. Rac, Artificial neural network based fault diagnostic system for electric power distribution feeders, Electric Power Systems Research, 35, 1995, 1–10. doi:10.1016/0378-7796(95)00990-6
  10. [10] L.A. Snider & Y.Y. Shan, The artificial neural networks based relay algorithm for distribution system high impedance fault detection, Proc. 4th Int. Conf. on Advances in Power System Control, Operation and Management, Hong Kong, 1997, 100–106. doi:10.1049/cp:19971812
  11. [11] P.L. Mao & 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, 11 (4), 2001, 654–660. doi:10.1109/61.956753
  12. [12] F. Martin & J.A. Aguado, Wavelet-based ANN approach for transmission line protection, IEEE Transactions on Power Delivery, 18 (4), 2003, 1572–1574. doi:10.1109/TPWRD.2003.817523
  13. [13] C.S. Burrus, R.A. Gopinath, & H. Guo, Introduction to wavelet and wavelet transforms: A primer, Filter banks and the discrete wavelet transform (New Jersey: Prentice-Hall, 1998), 31–32.
  14. [14] I.K. Yu & Y.H. Song, Development of novel adaptive single pole autoreclosure schemes for extra high voltage transmission systems using wavelet transform analysis, Electrical Power Systems Research, 47, 1998, 11–19. doi:10.1016/S0378-7796(98)00034-0
  15. [15] C.-H. Kim, H. Kim, Y.-H. Ko, S.-H. Byun, P.K. Aggarwal, & 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, 17 (4), 2002, 921–929. doi:10.1109/TPWRD.2002.803793
  16. [16] Electricite de France (EDF) R&D, ARENE user’s guide version V3.0, MODELS-The fault, (2002) 218–227.
  17. [17] E.D. Sontag, Feedback stabilization using two-hidden-layer nets, IEEE Transactions on Neural Networks, 3 (6), 1992, 981–990. doi:10.1109/72.165599
  18. [18] E. Lokman, Artificial neural networks high impedance arcing fault detection, doctoral diss., Dept. of Electrical, Computer and Systems Eng., Faculty of Rensselaer Polytechnic Institute, 2003.
  19. [19] J.A. Freeman & D.M. Skapura, Neural networks: algorithms, applications and programming techniques (Addison-Wesley Pub. Co., 1991).
  20. [20] R.J. Abrahart, L. See, & P.E. Kneale, New tools for neurohydrologists: Using network pruning and model breeding algorithms to discover optimum inputs and architectures, Proc. 3rd Int. Conf. on Geocomputation, University of Bristol, 1998.
  21. [21] T.Y. Kwok & D.Y. Yeung, Constructive algorithms for structure learning in feedforward neural networks for regression problem, IEEE Transactions on Neural Networks, 3, 1997, 630–645. doi:10.1109/72.572102
  22. [22] K. Swingler, Applying neural networks: A practical guide, Building a network (Academic Press, 1996) 51–76.

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