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

Stavrakakis Georgios, Zervakis Michalis, Z. E. Gketsis

Πλήρης Εγγραφή

URI: http://purl.tuc.gr/dl/dias/3B46CE3C-3FD2-4C10-BAA0-79A6AF0B7BAC
Έτος 2007
Τύπος Αφίσα σε Συνέδριο
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά Z. 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.2007.383573 https://doi.org/10.1109/IEMDC.2007.383573
Εμφανίζεται στις Συλλογές
Δημοσιεύσεις σε Συνέδρια στην Κοινότητα Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών

Περίληψη

This 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.

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