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Detection and classification of winding faults in windmill generators usingwavelet transform and ANN

Stavrakakis Georgios, Zervakis Michalis, Z. E. Gketsis

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URIhttp://purl.tuc.gr/dl/dias/3B46CE3C-3FD2-4C10-BAA0-79A6AF0B7BAC-
Identifierhttps://doi.org/10.1109/IEMDC.2007.383573-
Languageen-
Extent6 pagesen
TitleDetection and classification of winding faults in windmill generators using wavelet transform and ANNen
CreatorStavrakakis Georgiosen
CreatorΣταυρακακης Γεωργιοςel
CreatorZervakis Michalisen
CreatorΖερβακης Μιχαληςel
CreatorZ. E. Gketsisen
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryThis paper introduces the Wavelet Transform (WT) and Artificial Neural Networks (ANN) analysis to the diagnostics of electrical machines winding faults. A novel application is presented, exploring the potential of automatically identifying short circuits of windings, which often appear during machine manufacturing and operation. The early detection and classification of winding failures is of particular importance, as these kinds of defects can lead to winding damage due to overheating, imbalance, etc. The ANN approach is proven effective in detecting and classifying faults based on WT features extracted from high frequency measurements of the admittance, current, or voltage responses.en
Type of ItemΑφίσα σε Συνέδριοel
Type of ItemConference Posteren
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-15-
Date of Publication2007-
Bibliographic CitationZ. E. Gketsis, M.E Zervakis, G. Stavrakakis, "Detection and classification of winding faults in windmill generators using wavelet transform and ANN," in 2007 IEEE-Intern.Conf. Electric Mach. and Drives ,pp.178 - 183.doi: 10.1109/IEMDC.2007.383573en

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