A Study on the Gustafson-Kessel Clustering Algorithm in Power System Fault Identification

  • Abdullah, Amalina (Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University) ;
  • Banmongkol, Channarong (Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University) ;
  • Hoonchareon, Naebboon (Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University) ;
  • Hidaka, Kunihiko (Department of Electrical Engineering and Information System, University of Tokyo)
  • Received : 2016.05.18
  • Accepted : 2017.06.26
  • Published : 2017.09.01


This paper presents an approach of the Gustafson-Kessel (GK) clustering algorithm's performance in fault identification on power transmission lines. The clustering algorithm is incorporated in a scheme that uses hybrid intelligent technique to combine artificial neural network and a fuzzy inference system, known as adaptive neuro-fuzzy inference system (ANFIS). The scheme is used to identify the type of fault that occurs on a power transmission line, either single line to ground, double line, double line to ground or three phase. The scheme is also capable an analyzing the fault location without information on line parameters. The range of error estimation is within 0.10 to 0.85 relative to five values of fault resistances. This paper also presents the performance of the GK clustering algorithm compared to fuzzy clustering means (FCM), which is particularly implemented in structuring a data. Results show that the GK algorithm may be implemented in fault identification on power system transmission and performs better than FCM.



  1. Z. M. Radojevic et al., "New Approach for Fault Location on Transmission Lines Not Requirig Line Parameters", International Conference on Power Systems Transients (IPST2009), Kyoto, Japan June 3-6, 2009.
  2. J. C. Bezdek, "Pattern Recognition with Fuzzy Objective Function Algorithms", Plenum Press, New York, 1981.
  3. J. C. Bezdek and S. K. Pal, "Fuzzy Models for Pattern Recognition: Methods that Search for Structure in Data", IEEE Press, New York, 1992.
  4. Raghu Krishnapuram and Jongwoo Kim, "A Note on the Gustafson-Kessel and Adaptive Fuzzy Clustering Algorithms", IEEE Transactions On Fuzzy Systems. vol. 7, no. 4, August 1999.
  5. Reddy M. J. et. Al., "A Wavelet-Fuzzy Combined Approach for Classification and Location of Transmission Line Faults", Electrical Power & Energy Systems, vol. 29, no. 1, pp. 669-678, 2007
  6. Dash P.K., Pradhan A.K., and Panda G., "A Novel Fuzzy Neural Network Based Distance Relaying Scheme", IEEE Trans. Power Delivery, vol. 15, no. 3, pp. 902-907, 2000.
  7. Javad Sadeh et. Al, "A New And Accurate Fault Location Algorithm For Combined Transmission Line Using ANFIS", ELSEVIER, vol. 79, pp. 1538-1545, 2009.
  8. P. C. Panchariya et al, "Nonlinear System Identification Using Takagi-Sugeno Type Neuro-Fuzzy Model", Second IEEE International Conference on Intelligent Systems, 2004.
  9. Sato-Ilic, Mika; Jain, Lakhmi C., "Innovations in Fuzzy Clustering", Electronic Book, 2006
  10. F. Magnago and A. Abur, "Fault Location Using Wavelets", IEEE Transactions on Power Delivery, vol. 13, Issue 4, October 1998.
  11. Zhao, S.Y., "Calculus and Clustering", China Renming University Press, 1987.
  12. Gustafson, D. E., Kessel, W., "Fuzzy Clustering with a Fuzzy Covariance Matrix", In: Proceedings of IEEE Conferenceon Decision Control, San Diego, CA, pp. 761-766, 1979.
  13. Dave, R.N., "Boundary Detection Through Fuzzy Clustering", In IEEE International Conference on Fuzzy Systems, pages 127-134, San Diego, USA, 1992.
  14. Babuska, R., "Fuzzy Modeling for Control", Kluwer Academic Publishers, Boston, USA, 1998
  15. Wu, K. L., Yang, M. S., "Alternative C-Means Clustering Algorithms", Pattern Recog. 35, 2267-2278, 2002.
  16. Babu.ska, R., P.J. Van der Veen and U. Kaymak,. "Improved Covariance Estimation For Gustafson-Kessel Clustering," International Conference on Fuzzy Systems, Honolulu, Hawaii, pp. 1081-1085, 2002.
  17. Hoppner, et. Al.: "Fuzzy Cluster Analysis", John Wiley & Sons, Chichester, England, 1999.
  18. Paasche, et. Al, "Rapid Integration of Large Airborne Geophysical Data Suites Using a Fuzzy Partitioning Cluster Algorithm" Exploration Geophysics 40, 277-287, 2009.
  19. Zhang Qing-Chao et. Al, "Fault Location of Two-Parallel Transmission Line For Double Phase-to-Earth Fault Using One-Terminal Data", Journal of Zhejiang University, vol. 4, no. 5, pp. 520-525, 2003.
  20. Ramadoni Syahputra, "A Neuro-fuzzy Approach For the Fault Location Estimation on Unsynchronized Two-Terminal Transmission Line", Int. Journal of Computer Science & Information Technology, vol. 5, no. 1, February 2013.