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

Backpropagation; 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; R...

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Main Authors: Yaw C.T., Teoh S.L., Koh S.P., Yap K.S., Chong K.H., Low F.W.
Other Authors: 36560884300
Format: Conference Paper
Published: Institute of Physics 2023
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id my.uniten.dspace-27125
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spelling my.uniten.dspace-271252023-05-29T17:39:54Z Fault Diagnosis in Wind Energy Management System using Extreme Learning Machine: A Systematic Review Yaw C.T. Teoh S.L. Koh S.P. Yap K.S. Chong K.H. Low F.W. 36560884300 57069662700 57883863700 24448864400 36994481200 56513524700 Backpropagation; 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 analysis Fault 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. Final 2023-05-29T09:39:53Z 2023-05-29T09:39:53Z 2022 Conference Paper 10.1088/1742-6596/2319/1/012014 2-s2.0-85137685692 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137685692&doi=10.1088%2f1742-6596%2f2319%2f1%2f012014&partnerID=40&md5=b3f83fb921de04af0877d1489835d844 https://irepository.uniten.edu.my/handle/123456789/27125 2319 1 12014 All Open Access, Gold Institute of Physics Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Backpropagation; 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 analysis
author2 36560884300
author_facet 36560884300
Yaw C.T.
Teoh S.L.
Koh S.P.
Yap K.S.
Chong K.H.
Low F.W.
format Conference Paper
author Yaw C.T.
Teoh S.L.
Koh S.P.
Yap K.S.
Chong K.H.
Low F.W.
spellingShingle Yaw C.T.
Teoh S.L.
Koh S.P.
Yap K.S.
Chong K.H.
Low F.W.
Fault Diagnosis in Wind Energy Management System using Extreme Learning Machine: A Systematic Review
author_sort Yaw C.T.
title Fault Diagnosis in Wind Energy Management System using Extreme Learning Machine: A Systematic Review
title_short Fault Diagnosis in Wind Energy Management System using Extreme Learning Machine: A Systematic Review
title_full Fault Diagnosis in Wind Energy Management System using Extreme Learning Machine: A Systematic Review
title_fullStr Fault Diagnosis in Wind Energy Management System using Extreme Learning Machine: A Systematic Review
title_full_unstemmed Fault Diagnosis in Wind Energy Management System using Extreme Learning Machine: A Systematic Review
title_sort fault diagnosis in wind energy management system using extreme learning machine: a systematic review
publisher Institute of Physics
publishDate 2023
_version_ 1806427934002511872
score 13.188404

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