Neural Computing and Applications

, Volume 31, Issue 12, pp 9127–9143 | Cite as

High-impedance fault detection in medium-voltage distribution network using computational intelligence-based classifiers

  • Veerapandiyan VeerasamyEmail author
  • Noor Izzri Abdul Wahab
  • Rajeswari Ramachandran
  • Mariammal Thirumeni
  • Chitra Subramanian
  • Mohammad Lutfi Othman
  • Hashim Hizam
Original Article


This paper presents the high-impedance fault (HIF) detection and identification in medium-voltage distribution network of 13.8 kV using discrete wavelet transform (DWT) and intelligence classifiers such as adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM). The three-phase feeder network is modelled in MATLAB/Simulink to obtain the fault current signal of the feeder. The acquired fault current signal for various types of faults such as three-phase fault, line to line, line to ground, double line to ground and HIF is sampled using 1st, 2nd, 3rd, 4th and 5th level of detailed coefficients and approximated by DWT analysis to extract the feature, namely standard deviation (SD) values, considering the time-varying fault impedance. The SD values drawn by DWT technique have been used to train the computational intelligence-based classifiers such as fuzzy, Bayes, multi-layer perceptron neural network, ANFIS and SVM. The performance indices such as mean absolute error, root mean square error, kappa statistic, success rate and discrimination rate are compared for various classifiers presented. The results showed that the proffered ANFIS and SVM classifiers are more effective and their performance is substantially superior than other classifiers.


High-impedance fault Discrete wavelet transform Adaptive neuro-fuzzy inference system Support vector machine Multi-layer perceptron neural network (MLP) Bayes and fuzzy classifier 



The author thanks the centre for Advanced Lightning, Power and Energy Research (ALPER), University Putra Malaysia (UPM) for the fund under (9630000). Also we thank the potential reviewer for their valuable comments to improve the quality of the paper.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Advanced Lightning, Power and Energy Research (ALPER), Department of Electrical and Electronics Engineering, Faculty of EngineeringUniversiti Putra Malaysia (UPM)SerdangMalaysia
  2. 2.Department of Electrical EngineeringRajalakshmi Engineering CollegeChennaiIndia
  3. 3.Department of Electrical EngineeringGovernment College of TechnologyCoimbatoreIndia

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