Publication:
Classification of Faults Due to Transient Overvoltage Source Using Artificial Neural Network

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dc.contributor.authorShapiyan N.S.en_US
dc.contributor.authorAziz N.F.A.en_US
dc.contributor.authorYasin Z.M.en_US
dc.contributor.authorSalim N.A.en_US
dc.contributor.authorid57253302400en_US
dc.contributor.authorid57221906825en_US
dc.contributor.authorid57211410254en_US
dc.contributor.authorid36806685300en_US
dc.date.accessioned2023-05-29T09:06:16Z
dc.date.available2023-05-29T09:06:16Z
dc.date.issued2021
dc.descriptionElectric power supplies to apparatus; Electrolysis; Lightning; Direct strike; Equipment damage; Fault classification; Lightning faults; Lightning strikes; Natural phenomena; Permanent damage; Transient over-voltage; Neural networksen_US
dc.description.abstractFault is a common problem in power system and classifying the cause would be helpful to minimize the risk of permanent damage to the equipment and increase the quality of the power supply. Transient in power system is one of the causes of fault because it takes time to be discovered and the impact is clear when the severity is at the worst such as the equipment is totally damaged and cannot be fixed anymore. Lightning strikes is a natural phenomenon that can produce transient in power system and eventually produce fault. Most of lightning cases are direct strike to equipment that result to exceeding the threshold equipment limit. However, faults due to lightning are easily mistakenly classified since the strikes could be indirect and the time taken to identify the fault could be affected by other factors such as external contact by animals. This paper investigates type of faults due to transient overvoltage source and fault classification method by using Artificial Neural Network. Initially, the data from EPRI's website is extracted and analysed before it can be initialized as input data in MATLAB. Since faults due to lightning can lead to equipment damage, this paper classifies faults based on lightning and non-lightning. The results obtained have shown that the developed method is able to classify fault types to either lightning or non-lightning faults. � 2021 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICSGRC53186.2021.9515230
dc.identifier.epage310
dc.identifier.scopus2-s2.0-85114633263
dc.identifier.spage305
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85114633263&doi=10.1109%2fICSGRC53186.2021.9515230&partnerID=40&md5=0d139602e438894114c8c68014523427
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26041
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2021 IEEE 12th Control and System Graduate Research Colloquium, ICSGRC 2021 - Proceedings
dc.titleClassification of Faults Due to Transient Overvoltage Source Using Artificial Neural Networken_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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