Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Florkowski, M., Furgal, J.: Detection of windings faults in electrical machines using the frequency response analysis method. Meas. Sci. Technol. 15, 2067–2074 (2004)
Leibfriend, T., Christian, J., Feser, K.: Transfer function method to diagnose axial displacement and radial deformation of transformer windings. IEEE Trans. Power Deliv. 18, 493–505 (2003)
Grandi, G., Casadei, D., Reggiani, U.: Equivalent circuit of mush wound AC windings for high frequency analysis. In: Proc. ISIE Conf., pp. 201–206 (1997)
Keppel, G., Zedeck, S.: Data Analysis for Research Designs-Analysis of Variance and Multiple regression/Correlation Approaches. W.H. Freeman and Company, New York (1989)
Daubechies, I.: The Wavelet Transform, Time Frequency Localization and Signal Analysis. IEEE Trans. on Info. Theory 36(5), 961–1005 (1990)
Nawap, S.H., Quatieri, T.F., Limand, J.S., Oppenheim, A.V.: Short Time Fourier Transform, pp. 239–337. Prentice-Hall, Englewood Cliffs (1988)
Kara, S., Dirgenali, F., Okkesim, S.: Detection of gastric dysrhythmia using WT and ANN in diabetic gastroparesis patients. Computers in Biology and Medicine 36, 276–290 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gketsis, Z., Zervakis, M., Stavrakakis, G. (2006). Early Detection of Winding Faults in Windmill Generators Using Wavelet Transform and ANN Classification. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_78
Download citation
DOI: https://doi.org/10.1007/11840930_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
Online ISBN: 978-3-540-38873-9
eBook Packages: Computer ScienceComputer Science (R0)