Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/3250
DC FieldValueLanguage
dc.contributor.advisorAkindeji, Timothy Kayode-
dc.contributor.advisorAdetiba, Emmanuel-
dc.contributor.authorOnaolapo, Adeniyi Kehindeen_US
dc.date.accessioned2019-07-15T08:48:36Z-
dc.date.available2019-07-15T08:48:36Z-
dc.date.issued2018-
dc.identifier.other712097-
dc.identifier.urihttp://hdl.handle.net/10321/3250-
dc.descriptionA dissertation submitted in fulfillment of the requirements for the degree of Master of Engineering: Electrical Engineering, Durban University of Technology, Durban, South Africa, 2018.en_US
dc.description.abstractElectrical power systems experience unforeseen faults attributable to diverse arbitrary reasons. Unanticipated failures occurring in power systems are to be prevented from propagating to other parts of the protective system to enhance economic efficacy of electric utilities and provide better service to energy consumers. Since most consumers are directly connected to power distribution networks, there is an increasing research efforts in distribution network fault recognition and fault-types identifications to solve the problem of outages due to faults. This study focuses on fault recognition and fault-types identification in electrical power distribution system based on the Design Science Research (DSR) approach. Diverse simulations of fault types at different locations were applied to the IEEE 13 Node Test Feeder to produce three phase currents and voltages as data set for this study. This was realized by modelling the IEEE 13-node benchmark test feeder in MATLAB-Simulink R2017a. In order to achieve intelligent fault recognition and fault-type identification, different Multi-layer Perceptron Artificial Neural Networks (MLP-ANN) models were designed and subsequently trained using the generated dataset with the Neural Network toolbox in MATLAB R2017a. The fault recognition task verifies if a fault occurs or not while the fault-types identification task determines the fault class as well as the faulty phase(s). Results obtained from the various MLP-ANN models were recorded and statistically analyzed. Acceptable performances were obtained for fault recognition with the 6-25-20-15-1 MLP-ANN architecture, for fault-types identification with the 6-40-4 MLP-ANN architecture and for fault location with the 6-30-15-5-4 MLP-ANN architecture. Given the result obtained in this study, MLP-ANN is adjudged suitable for intelligent fault recognition and fault-types identification in power distribution systems. The trained MLP-ANNs in this study could ultimately be incorporated in power distribution networks within South Africa and beyond in order to enhance energy customers’ satisfaction.en_US
dc.description.sponsorshipNational Research Foundationen_US
dc.format.extent163 pen_US
dc.language.isoenen_US
dc.subject.lcshElectric power systems--Maintenance and repairen_US
dc.subject.lcshElectric fault locationen_US
dc.subject.lcshElectric power distribution--Automationen_US
dc.subject.lcshSmart power gridsen_US
dc.subject.lcshNeural networks (Computer science)en_US
dc.titleModeling and recognition of faults in smart distribution grid using maching intelligence techniqueen_US
dc.typeThesisen_US
dc.description.levelMen_US
dc.identifier.doihttps://doi.org/10.51415/10321/3250-
item.openairetypeThesis-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.languageiso639-1en-
item.fulltextWith Fulltext-
Appears in Collections:Theses and dissertations (Engineering and Built Environment)
Files in This Item:
File Description SizeFormat
ONAOLAPO_2018.pdf3.33 MBAdobe PDFThumbnail
View/Open
Show simple item record

Page view(s)

694
checked on Dec 13, 2024

Download(s)

594
checked on Dec 13, 2024

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.