Publication:
Fault classification in smart distribution network using support vector machine

Simple item page
dc.citedby14
dc.contributor.authorChuan O.W.en_US
dc.contributor.authorAb Aziz N.F.en_US
dc.contributor.authorYasin Z.M.en_US
dc.contributor.authorSalim N.A.en_US
dc.contributor.authorWahab N.A.en_US
dc.contributor.authorid57214806552en_US
dc.contributor.authorid57221906825en_US
dc.contributor.authorid57211410254en_US
dc.contributor.authorid36806685300en_US
dc.contributor.authorid35790572400en_US
dc.date.accessioned2023-05-29T08:14:07Z
dc.date.available2023-05-29T08:14:07Z
dc.date.issued2020
dc.description.abstractMachine learning application have been widely used in various sector as part of reducing work load and creating an automated decision making tool. This has gain the interest of power industries and utilities to apply machine learning as part of the operation. Fault identification and classification based machine learning application in power industries have gain significant accreditation due to its great capability and performance. In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. Eleven different types of faults are generated with respect to actual network. A wide range of simulation condition in terms of different fault impedance value as well as fault types are considered in training and testing data. Right setting parameters are important to learning results and generalization ability of SVM. Gaussian radial basis function (RBF) kernel function has been used for training of SVM to accomplish the most optimized classifier. Initial finding from simulation result indicates that the proposed method is quick in learning and shows good accuracy values on faults type classification in distribution system. The developed algorithm is tested on IEEE 34 bus and IEEE 123 bus test distribution system. Copyright � 2020 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijeecs.v18.i3.pp1148-1155
dc.identifier.epage1155
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85079183447
dc.identifier.spage1148
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85079183447&doi=10.11591%2fijeecs.v18.i3.pp1148-1155&partnerID=40&md5=30ed2df8f0923a31866e6202a9a35437
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25774
dc.identifier.volume18
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleIndonesian Journal of Electrical Engineering and Computer Science
dc.titleFault classification in smart distribution network using support vector machineen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS