Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
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| URI: | http://purl.tuc.gr/dl/dias/A89976CA-4FF4-4956-8545-25CEFFD10C58 | ||
| Έτος | 2006 | ||
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | ||
| Άδεια Χρήσης |
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| Βιβλιογραφική Αναφορά | Z. Gketsis, M. Zervakis, G. Stavrakakis ,"Early Detection of winding faults in windmill generators using wavelet transform and ANN classification,"in 2006 16th Int. Conf.,pp.746-756.doi:10.1007/11840930_78 https://doi.org/10.1007/11840930_78 | ||
| Εμφανίζεται στις Συλλογές |
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 that can appear during machine manufacturing and operation. Such faults are usually the result of the influence of electrodynamics forces generated during the flow of large short circuit currents, as well as of the forces occurring when the transformers or generators are transported. 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. Application results on investigations of windmill generator winding faults are presented. The ANN approach is proven effective in classifying faults based on features extracted by the WT.
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