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Detection and classification of winding faults in windmill generators using wavelet transform and ANN

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Detection and classification of winding faults in windmill generators using wavelet transform and ANN (EN)
Ζερβακης Μιχαλης (EL)
Σταυρακακης Γεωργιος (EL)
Zervakis Michalis (EN)
Z. E. Gketsis (EN)
Stavrakakis Georgios (EN)
conferenceItem
poster
2007
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. (EN)
IEEE-International Electric Machines and Drives Conference (EL)
English
Institute of Electrical and Electronics Engineers (EN)
Πολυτεχνείο Κρήτης (EL)
Technical University of Crete (EN)
Technical University of Crete
Institutional Repository Technical University of Crete
School of Production Engineering and Management - Conference Publications



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