Preprint Article Version 1 This version is not peer-reviewed

High Impedance Fault Detection in MV Distribution Network using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

Version 1 : Received: 29 October 2018 / Approved: 29 October 2018 / Online: 29 October 2018 (13:44:25 CET)

A peer-reviewed article of this Preprint also exists.

Veerasamy, V.; Abdul Wahab, N.I.; Ramachandran, R.; Mansoor, M.; Thirumeni, M.; Lutfi Othman, M. High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System. Energies 2018, 11, 3330. Veerasamy, V.; Abdul Wahab, N.I.; Ramachandran, R.; Mansoor, M.; Thirumeni, M.; Lutfi Othman, M. High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System. Energies 2018, 11, 3330.

Journal reference: Energies 2018, 11, 3330
DOI: 10.3390/en11123330

Abstract

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

Subject Areas

Discrete Wavelet Transform (DWT); Adaptive Neuro-Fuzzy Inference System (ANFIS); Fuzzy Logic system (FLS); High Impedance Fault (HIF).

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