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

Stavrakakis Georgios, Zervakis Michalis, Z. E. Gketsis

Απλή Εγγραφή

URIhttp://purl.tuc.gr/dl/dias/3B46CE3C-3FD2-4C10-BAA0-79A6AF0B7BAC-
Αναγνωριστικόhttps://doi.org/10.1109/IEMDC.2007.383573-
Γλώσσαen-
Μέγεθος6 pagesen
ΤίτλοςDetection and classification of winding faults in windmill generators using wavelet transform and ANNen
ΔημιουργόςStavrakakis Georgiosen
ΔημιουργόςΣταυρακακης Γεωργιοςel
ΔημιουργόςZervakis Michalisen
ΔημιουργόςΖερβακης Μιχαληςel
ΔημιουργόςZ. E. Gketsisen
ΕκδότηςInstitute of Electrical and Electronics Engineersen
ΠερίληψηThis 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
ΤύποςΑφίσα σε Συνέδριοel
ΤύποςConference Posteren
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-10-15-
Ημερομηνία Δημοσίευσης2007-
Βιβλιογραφική ΑναφοράZ. 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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