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Fault location in distribution systems using mathematical analysis and support vector machine / Sophi Shilpa Gururajapathy

Distribution systems are continuously exposed to fault occurrences due to various reasons, such as lightning strike, failure of power system components due to aging of equipment and human error. These phenomena affect the system reliability and results in expensive repairs, damaged work in process,...

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Main Author:	Sophi Shilpa, Gururajapathy
Format:	Thesis
Published:	2017
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://studentsrepo.um.edu.my/7446/1/All.pdf http://studentsrepo.um.edu.my/7446/6/sophi.pdf http://studentsrepo.um.edu.my/7446/
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id	my.um.stud.7446
record_format	eprints
spelling	my.um.stud.74462020-03-02T23:16:10Z Fault location in distribution systems using mathematical analysis and support vector machine / Sophi Shilpa Gururajapathy Sophi Shilpa, Gururajapathy TA Engineering (General). Civil engineering (General) Distribution systems are continuously exposed to fault occurrences due to various reasons, such as lightning strike, failure of power system components due to aging of equipment and human error. These phenomena affect the system reliability and results in expensive repairs, damaged work in process, lost productivity and power loss to customers. Due to this, various intelligent methods have been developed to locate fault in distribution system. However, fault location using intelligent methods is challenging since it requires training data for processing. The training data is commonly created by simulation, which is time consuming. Therefore, in this work, a fault location method based on previous work is proposed using limited simulation data. The existing method was improved by estimating voltage sag data using support vector machine, thus limiting the simulated data. Faulty section is identified by comparing the actual voltage sag data with the simulated and estimated voltage sag data. An improved ranking and Euclidean distance approach for fault distance is also presented. A method using SVM is also proposed to identify the faulty phase, fault type, faulty section and fault distance. By having these features, a more accurate and effective fault location can be obtained. The method identifies faulty phase and fault type using support vector classification analysis. Meanwhile, the faulty section and the fault distance are identified using support vector regression analysis. The effectiveness of the proposed method was tested on an actual TNB distribution network from Malaysia and SaskPower distribution network from Canada. The test cases were conducted for all types of fault and for various fault resistances. The test results have proven the effectiveness of the proposed method in locating fault under various conditions. It has shown improvement over the existing trigonometric methods in locating different types of faults and may serve as an alternative technique for estimating fault location in distribution networks. 2017 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/7446/1/All.pdf application/pdf http://studentsrepo.um.edu.my/7446/6/sophi.pdf Sophi Shilpa, Gururajapathy (2017) Fault location in distribution systems using mathematical analysis and support vector machine / Sophi Shilpa Gururajapathy. PhD thesis, University of Malaya. http://studentsrepo.um.edu.my/7446/
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Student Repository
url_provider	http://studentsrepo.um.edu.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Sophi Shilpa, Gururajapathy Fault location in distribution systems using mathematical analysis and support vector machine / Sophi Shilpa Gururajapathy
description	Distribution systems are continuously exposed to fault occurrences due to various reasons, such as lightning strike, failure of power system components due to aging of equipment and human error. These phenomena affect the system reliability and results in expensive repairs, damaged work in process, lost productivity and power loss to customers. Due to this, various intelligent methods have been developed to locate fault in distribution system. However, fault location using intelligent methods is challenging since it requires training data for processing. The training data is commonly created by simulation, which is time consuming. Therefore, in this work, a fault location method based on previous work is proposed using limited simulation data. The existing method was improved by estimating voltage sag data using support vector machine, thus limiting the simulated data. Faulty section is identified by comparing the actual voltage sag data with the simulated and estimated voltage sag data. An improved ranking and Euclidean distance approach for fault distance is also presented. A method using SVM is also proposed to identify the faulty phase, fault type, faulty section and fault distance. By having these features, a more accurate and effective fault location can be obtained. The method identifies faulty phase and fault type using support vector classification analysis. Meanwhile, the faulty section and the fault distance are identified using support vector regression analysis. The effectiveness of the proposed method was tested on an actual TNB distribution network from Malaysia and SaskPower distribution network from Canada. The test cases were conducted for all types of fault and for various fault resistances. The test results have proven the effectiveness of the proposed method in locating fault under various conditions. It has shown improvement over the existing trigonometric methods in locating different types of faults and may serve as an alternative technique for estimating fault location in distribution networks.
format	Thesis
author	Sophi Shilpa, Gururajapathy
author_facet	Sophi Shilpa, Gururajapathy
author_sort	Sophi Shilpa, Gururajapathy
title	Fault location in distribution systems using mathematical analysis and support vector machine / Sophi Shilpa Gururajapathy
title_short	Fault location in distribution systems using mathematical analysis and support vector machine / Sophi Shilpa Gururajapathy
title_full	Fault location in distribution systems using mathematical analysis and support vector machine / Sophi Shilpa Gururajapathy
title_fullStr	Fault location in distribution systems using mathematical analysis and support vector machine / Sophi Shilpa Gururajapathy
title_full_unstemmed	Fault location in distribution systems using mathematical analysis and support vector machine / Sophi Shilpa Gururajapathy
title_sort	fault location in distribution systems using mathematical analysis and support vector machine / sophi shilpa gururajapathy
publishDate	2017
url	http://studentsrepo.um.edu.my/7446/1/All.pdf http://studentsrepo.um.edu.my/7446/6/sophi.pdf http://studentsrepo.um.edu.my/7446/
_version_	1738506021898289152
score	13.211869

Fault location in distribution systems using mathematical analysis and support vector machine / Sophi Shilpa Gururajapathy

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