Publication:
Fault Diagnosis in Wind Energy Management System using Extreme Learning Machine: A Systematic Review

Simple item page
dc.contributor.authorYaw C.T.en_US
dc.contributor.authorTeoh S.L.en_US
dc.contributor.authorKoh S.P.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorChong K.H.en_US
dc.contributor.authorLow F.W.en_US
dc.contributor.authorid36560884300en_US
dc.contributor.authorid57069662700en_US
dc.contributor.authorid57883863700en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid36994481200en_US
dc.contributor.authorid56513524700en_US
dc.date.accessioned2023-05-29T09:39:53Z
dc.date.available2023-05-29T09:39:53Z
dc.date.issued2022
dc.descriptionBackpropagation; Electric fault currents; Energy management systems; Fault detection; Knowledge acquisition; Neural networks; Support vector machines; Wind power; Wind turbines; Back-propagation neural networks; Extreme learning machine; Fault data; Faults diagnosis; Learning machines; Real-world; Renewable energies; Renewable energy source; Support vectors machine; Systematic Review; Failure analysisen_US
dc.description.abstractFault diagnosis is increasingly important given the worldwide demand on wind energy as one of the promising renewable energy sources. This systematic review aimed to summarize the fault diagnosis using Extreme Learning Machine (ELM) on wind energy. Firstly, two databases (i.e. Engineering Village (EV) and IEEE Explore were searched to identify relevant articles, using three important keywords, including Extreme Learning Machine/ELM, fault and wind. Of the 14 included studies, only eight studies mentioned the use of sensor to collect vibration signals as the fault data. Sensors were commonly installed at four places (gearbox, generator, bearing, or rotor) in the included studies. Only nine studies used either single or fusion feature extractions for the fault data. Two types of ELM (i.e. single/multi-layered or hybrid-ELM) were identified to diagnose fault. In general, studies showed the superiority of the application of ELM in producing accuracy results in fault diagnosis of WT, compared to other algorithms. Future studies should incorporate the use of real-world data, and improve on the reporting on the methodological components of the study, to better inform on the usefulness of ELM for fault diagnosis in real-world wind energy settings. � Published under licence by IOP Publishing Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12014
dc.identifier.doi10.1088/1742-6596/2319/1/012014
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85137685692
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85137685692&doi=10.1088%2f1742-6596%2f2319%2f1%2f012014&partnerID=40&md5=b3f83fb921de04af0877d1489835d844
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27125
dc.identifier.volume2319
dc.publisherInstitute of Physicsen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleJournal of Physics: Conference Series
dc.titleFault Diagnosis in Wind Energy Management System using Extreme Learning Machine: A Systematic Reviewen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS