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Multiple fault detection in induction generator for grid-connected wind turbine application.

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Title: Multiple fault detection in induction generator for grid-connected wind turbine application.
Authors: Sboui, Ahmed1,2 (AUTHOR) ahmedsboui@yahoo.fr, Salah, Mohamed1,3 (AUTHOR), Bacha, Khmais1 (AUTHOR), Ghanmi, Nasr4 (AUTHOR), Chaari, Abdelkader1,5 (AUTHOR)
Source: Wind Engineering. Feb2026, Vol. 50 Issue 1, p177-199. 23p.
Subjects: Fault diagnosis, Induction generators, Rotor dynamics, Renewable energy sources, Spectrum analysis instruments, Wind turbines
Abstract: While numerous studies have investigated the diagnosis of squirrel-cage induction machines, most have focused on the motor mode. This work extends the diagnostic scope to the generator operation. Two fault scenarios are addressed: a mechanical unbalance and a combined condition where this defect coexists with incipient Rotor Electrical Asymmetry (REA). A theoretical framework is developed to analyze the spectral behavior of stator currents under these fault conditions across both operating modes. To enhance detection sensitivity, we introduce an innovative frequency offset spectrum combination technique, which combines the stator current spectrum with its frequency-shifted versions to amplify fault-related components. Experimental validation on a laboratory test rig confirms that the proposed approach significantly improves sensitivity to incipient REA faults in generator mode operation, highlighting its potential for early fault detection in real-world applications. [ABSTRACT FROM AUTHOR]
Copyright of Wind Engineering is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 191348507
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PubTypeId: academicJournal
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  Data: Multiple fault detection in induction generator for grid-connected wind turbine application.
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  Data: <searchLink fieldCode="AR" term="%22Sboui%2C+Ahmed%22">Sboui, Ahmed</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> ahmedsboui@yahoo.fr</i><br /><searchLink fieldCode="AR" term="%22Salah%2C+Mohamed%22">Salah, Mohamed</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bacha%2C+Khmais%22">Bacha, Khmais</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ghanmi%2C+Nasr%22">Ghanmi, Nasr</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chaari%2C+Abdelkader%22">Chaari, Abdelkader</searchLink><relatesTo>1,5</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Wind+Engineering%22">Wind Engineering</searchLink>. Feb2026, Vol. 50 Issue 1, p177-199. 23p.
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  Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Induction+generators%22">Induction generators</searchLink><br /><searchLink fieldCode="DE" term="%22Rotor+dynamics%22">Rotor dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink><br /><searchLink fieldCode="DE" term="%22Spectrum+analysis+instruments%22">Spectrum analysis instruments</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+turbines%22">Wind turbines</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: While numerous studies have investigated the diagnosis of squirrel-cage induction machines, most have focused on the motor mode. This work extends the diagnostic scope to the generator operation. Two fault scenarios are addressed: a mechanical unbalance and a combined condition where this defect coexists with incipient Rotor Electrical Asymmetry (REA). A theoretical framework is developed to analyze the spectral behavior of stator currents under these fault conditions across both operating modes. To enhance detection sensitivity, we introduce an innovative frequency offset spectrum combination technique, which combines the stator current spectrum with its frequency-shifted versions to amplify fault-related components. Experimental validation on a laboratory test rig confirms that the proposed approach significantly improves sensitivity to incipient REA faults in generator mode operation, highlighting its potential for early fault detection in real-world applications. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Wind Engineering is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1177/0309524X251400194
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 177
    Subjects:
      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Induction generators
        Type: general
      – SubjectFull: Rotor dynamics
        Type: general
      – SubjectFull: Renewable energy sources
        Type: general
      – SubjectFull: Spectrum analysis instruments
        Type: general
      – SubjectFull: Wind turbines
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      – TitleFull: Multiple fault detection in induction generator for grid-connected wind turbine application.
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            NameFull: Salah, Mohamed
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            NameFull: Ghanmi, Nasr
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            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
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