Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

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

Stavrakakis Georgios, Zervakis Michalis, Z. E. Gketsis

Full record


URI: http://purl.tuc.gr/dl/dias/3B46CE3C-3FD2-4C10-BAA0-79A6AF0B7BAC
Year 2007
Type of Item Conference Poster
License
Details
Bibliographic Citation 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
Appears in Collections

Summary

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.

Services

Statistics