Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/114153
DC Field | Value | Language |
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dc.contributor.author | Alipoor, Ghasem | - |
dc.contributor.author | Mirbagheri, Seyed Jafar | - |
dc.contributor.author | Moosavi, Seyed Mohammad Mahdi | - |
dc.contributor.author | Cruz, Sérgio M. A. | - |
dc.date.accessioned | 2024-03-22T09:22:21Z | - |
dc.date.available | 2024-03-22T09:22:21Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 1751-8660 | pt |
dc.identifier.issn | 1751-8679 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/114153 | - |
dc.description.abstract | Wind turbines are increasingly expanding worldwide and Doubly‐Fed Induction Generator (DFIG) is a key component of most of them. Stator winding fault is a major fault in this equipment and its incipient detection is of vital importance. However, there is a paucity of research in this field. In this study, a novel machine learning‐based method is proposed for incipient detection of inter‐turn short‐circuit fault (ITF) in the DFIG stator based on the current signals of the stator. The proposed method makes use of state‐ofthe‐ art deep learning methods along with conventional signal processing tools and general machine learning techniques. More specifically, the incipient fault detection problem is regarded as a multi‐class classification problem and a Long Short‐Term Memory network, which is more appropriate for time‐series data is utilised for inference. Furthermore, a variant of the celebrated Empirical mode Decomposition analysis tool is used to extract some well‐known statistical features among which the most informative ones are selected using a new feature selection method. Our tests using experimental data in steady‐state conditions show that the proposed method can accurately detect ITF fault at its initial stage when only one turn is shorted. Moreover, its performance is considerably higher than that of a variety of machine learning‐based methods. | pt |
dc.language.iso | eng | pt |
dc.publisher | Wiley-Blackwell | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt |
dc.subject | deep learning | pt |
dc.subject | doubly‐fed induction generator (DFIG) | pt |
dc.subject | empirical mode decomposition (EMD) | pt |
dc.subject | fault detection | pt |
dc.subject | feature selection | pt |
dc.subject | inter‐turn short‐circuit fault | pt |
dc.title | Incipient detection of stator inter‐turn short‐circuit faults in a Doubly‐Fed Induction Generator using deep learning | pt |
dc.type | article | - |
degois.publication.firstPage | 256 | pt |
degois.publication.lastPage | 267 | pt |
degois.publication.issue | 2 | pt |
degois.publication.title | IET Electric Power Applications | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.1049/elp2.12262 | pt |
degois.publication.volume | 17 | pt |
dc.date.embargo | 2022-01-01 | * |
uc.date.periodoEmbargo | 0 | pt |
item.fulltext | Com Texto completo | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
crisitem.author.researchunit | IT - Institute of Telecommunications | - |
crisitem.author.orcid | 0000-0002-9651-8925 | - |
Appears in Collections: | FCTUC Eng.Electrotécnica - Artigos em Revistas Internacionais |
Files in This Item:
File | Description | Size | Format | |
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IET Electric Power Appl - 2022 - Alipoor - Incipient detection of stator inter‐turn short‐circuit faults in a Doubly‐Fed.pdf | 1.77 MB | Adobe PDF | View/Open |
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