International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Internal Fault Classification in Transformer Windings using Combination of Discrete Wavelet-Transforms and Back-propagation Neural Networks

  • Ngaopitakkul Atthapol (Department of Electrical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang) ;
  • Kunakorn Anantawat (Department of Electrical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang)
  • Published : 2006.06.01
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Abstract

This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and neural networks for detection and classification of internal faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using ATP/EMTP in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented using toolboxes on MATLAB/Simulink. Various cases and fault types based on Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. It is found that the proposed method gives a satisfactory accuracy, and will be particularly useful in a development of a modern differential relay for a transformer protection scheme.

Keywords

References

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