Early Detection of Winding Faults in Windmill Generators Using Wavelet Transform and ANN Classification

  • Zacharias Gketsis
  • Michalis Zervakis
  • George Stavrakakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


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.


Artificial Neural Network Fast Fourier Transform Fault Detection Wavelet Transform Detail Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zacharias Gketsis
    • 1
  • Michalis Zervakis
    • 1
  • George Stavrakakis
    • 1
  1. 1.Electronic and Computer Engineering Dept.Technical University of CreteChania CreteGreece

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