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Fault classification and location for distribution generation using artificial neural networks

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dc.contributor.authorHong F.K.en_US
dc.contributor.authorKeen Raymond W.J.en_US
dc.contributor.authorHeong O.K.en_US
dc.contributor.authorMei Kuan T.en_US
dc.contributor.authorid57221910587en_US
dc.contributor.authorid55193255600en_US
dc.contributor.authorid55096903900en_US
dc.contributor.authorid57220873063en_US
dc.date.accessioned2023-05-29T08:06:40Z
dc.date.available2023-05-29T08:06:40Z
dc.date.issued2020
dc.descriptionDistributed power generation; Forecasting; Location; Machine learning; Neural networks; Bus networks; Distributed networks; Distribution generation; Fault classification; Fault distance; Fault sections; Location method; Three categories; Complex networksen_US
dc.description.abstractWith the proliferation of distributed generation (DG), the distributed network had become more complex. Such complexity will lead to difficulty for fault location in the distributed network. It may degrade the precision of existing fault location methods. Therefore, this paper will investigate the impact of distributed generation toward machine learning (ML) based fault location. Three categories of fault location had been tested which is fault type prediction, fault section prediction, and fault distance prediction with and without DG presence. The accuracy of machine learning based fault location is verified in IEEE 16 bus network and the impact due to the presence of DG, represented using photovoltaic (PV) generator is discussed in detail. � 2020 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9314535
dc.identifier.doi10.1109/PECon48942.2020.9314535
dc.identifier.epage320
dc.identifier.scopus2-s2.0-85100588405
dc.identifier.spage315
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85100588405&doi=10.1109%2fPECon48942.2020.9314535&partnerID=40&md5=7192ad3644e48e012973b59138838f44
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25077
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitlePECon 2020 - 2020 IEEE International Conference on Power and Energy
dc.titleFault classification and location for distribution generation using artificial neural networksen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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