High impedance fault detection and isolation in power distribution networks using support vector machines

This paper proposes an accurate High Impedance Fault (HIF) detection and isolation scheme in a power distribution network. The proposed scheme utilizes the data available from voltage and current sensors. The technique employs multiple algorithms consisting of Principal Component Analysis, Fisher Di...

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Main Authors: Muhammad Sarwar, Faisal Mehmood, Muhammad Abid, Abdul Qayyum Khan, Sufi Tabassum Gul, Adil Sarwar Khan
Format: Article
Language:English
Published: Elsevier 2020-12-01
Series:Journal of King Saud University: Engineering Sciences
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1018363919302004
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spelling doaj-4455b59aeb7c4830b1ca1d49258e19402020-12-23T04:58:46ZengElsevierJournal of King Saud University: Engineering Sciences1018-36392020-12-01328524535High impedance fault detection and isolation in power distribution networks using support vector machinesMuhammad Sarwar0Faisal Mehmood1Muhammad Abid2Abdul Qayyum Khan3Sufi Tabassum Gul4Adil Sarwar Khan5Corresponding author.; Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, Islamabad, PakistanDepartment of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, Islamabad, PakistanDepartment of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, Islamabad, PakistanDepartment of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, Islamabad, PakistanDepartment of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, Islamabad, PakistanDepartment of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, Islamabad, PakistanThis paper proposes an accurate High Impedance Fault (HIF) detection and isolation scheme in a power distribution network. The proposed scheme utilizes the data available from voltage and current sensors. The technique employs multiple algorithms consisting of Principal Component Analysis, Fisher Discriminant Analysis, Binary and Multiclass Support Vector Machine for detection and identification of the high impedance fault. These data-driven techniques have been tested on IEEE 13-node distribution network for detection and identification of high impedance faults with the broken and unbroken conductor. Further, the robustness of machine learning techniques has also been analysed by examining their performance with variation in loads for different faults. Simulation results for different faults at various locations have shown that proposed methods are fast and accurate in diagnosing high impedance faults. Multiclass Support Vector Machine gives the best result to detect and locate high impedance faults accurately. It ensures reliability, security and dependability of the distribution network.http://www.sciencedirect.com/science/article/pii/S1018363919302004Fisher Discriminant AnalysisHigh impedance faultPrincipal Component AnalysisSupport vector machines
collection DOAJ
language English
format Article
sources DOAJ
author Muhammad Sarwar
Faisal Mehmood
Muhammad Abid
Abdul Qayyum Khan
Sufi Tabassum Gul
Adil Sarwar Khan
spellingShingle Muhammad Sarwar
Faisal Mehmood
Muhammad Abid
Abdul Qayyum Khan
Sufi Tabassum Gul
Adil Sarwar Khan
High impedance fault detection and isolation in power distribution networks using support vector machines
Journal of King Saud University: Engineering Sciences
Fisher Discriminant Analysis
High impedance fault
Principal Component Analysis
Support vector machines
author_facet Muhammad Sarwar
Faisal Mehmood
Muhammad Abid
Abdul Qayyum Khan
Sufi Tabassum Gul
Adil Sarwar Khan
author_sort Muhammad Sarwar
title High impedance fault detection and isolation in power distribution networks using support vector machines
title_short High impedance fault detection and isolation in power distribution networks using support vector machines
title_full High impedance fault detection and isolation in power distribution networks using support vector machines
title_fullStr High impedance fault detection and isolation in power distribution networks using support vector machines
title_full_unstemmed High impedance fault detection and isolation in power distribution networks using support vector machines
title_sort high impedance fault detection and isolation in power distribution networks using support vector machines
publisher Elsevier
series Journal of King Saud University: Engineering Sciences
issn 1018-3639
publishDate 2020-12-01
description This paper proposes an accurate High Impedance Fault (HIF) detection and isolation scheme in a power distribution network. The proposed scheme utilizes the data available from voltage and current sensors. The technique employs multiple algorithms consisting of Principal Component Analysis, Fisher Discriminant Analysis, Binary and Multiclass Support Vector Machine for detection and identification of the high impedance fault. These data-driven techniques have been tested on IEEE 13-node distribution network for detection and identification of high impedance faults with the broken and unbroken conductor. Further, the robustness of machine learning techniques has also been analysed by examining their performance with variation in loads for different faults. Simulation results for different faults at various locations have shown that proposed methods are fast and accurate in diagnosing high impedance faults. Multiclass Support Vector Machine gives the best result to detect and locate high impedance faults accurately. It ensures reliability, security and dependability of the distribution network.
topic Fisher Discriminant Analysis
High impedance fault
Principal Component Analysis
Support vector machines
url http://www.sciencedirect.com/science/article/pii/S1018363919302004
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