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.


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