A New Approach for Faults Detection and Classification in Overhead Line Systems Using Multiple Methods

Saleh Abdulsamad Ben Safar

doi:10.15866/iree.v15i5.16800

A New Approach for Faults Detection and Classification in Overhead Line Systems Using Multiple Methods

Saleh Abdulsamad Ben Safar^(1*)

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/iree.v15i5.16800

Abstract

Faults in power systems can be described by a sudden increase in current compared with the flow of normal current. The phenomenon of fault affects energy quality, security, and reliability of the system. Several events play a key role in causing faults such as trees falling on overhead lines, insulation breakdown, and lightning strikes. Thus, determining the location of faults is a very important subject, especially in Power Distribution System’s and this can be achieved by applying several methods to classify and detect a fault in order to identify the correct type of faults. this paper, Clarke Method Transformation and Phase Angle Shift techniques are used to classify faults in Power Distribution System and to determine if the fault is grounded or ungrounded. On the other hand, Smooth Support Vector Machine and Discrete Wavelet Transform techniques are used to determine the accurate fault types. The data taken are the faulted voltage phases, which are stored and imported in MATLAB from ATPDraw, which design and build the proposed system of 33 kV power supply, overhead lines with 20 km length and frequency of 50 Hz. The obtained results from MATLAB simulation demonstrate that the proposed algorithm works very properly and it can detect and classify the existence of faults within the transmission line in the Power Distribution System. The inclusion of the multi-phase faults is the future step to enhance the performance of this work.
Copyright © 2020 Praise Worthy Prize - All rights reserved.

Keywords

Overhead lines; Phase Angle Shift; Clarke Method Transformation; Discrete Wavelet Transform; Smooth Support Vector Machine

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