Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
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EL
| URI: | http://purl.tuc.gr/dl/dias/E919C475-D816-44A0-BCB9-A8B14562BAB9 | ||
| Έτος | 2009 | ||
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | ||
| Άδεια Χρήσης |
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| Βιβλιογραφική Αναφορά | ANN Z.E. Gketsis, M.E. Zervakis, G. Stavrakakis ," Detection and classification of winding faults in windmill generators using wavelet transform and ANN ," Electric Power Syst. Res.,vol. 79,no.11 ,pp. 1483-1494,2009.doi:10.1016/j.epsr.2009.05.001 https://doi.org/10.1016/j.epsr.2009.05.001 | ||
| Εμφανίζεται στις Συλλογές |
This paper exploits the Wavelet Transform (WT) analysis along with Artificial Neural Networks (ANN) for the diagnosis of electrical machines winding faults. A novel application is presented exploring the problem of automatically identifying short circuits of windings, which often appear during machine manufacturing and operation. Such faults are usually resulting from electrodynamics forces generated during the flow of large short circuit currents, as well as forces occurring when the machines 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 and investigations of windmill generator winding turn-to-turn faults between adjacent winding wires are presented. 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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