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Wavelet Packet Transform and Support Vector Machine Based Discrimination of Power Transformer Inrush Current from Internal Fault Currents

Author

Listed:
  • Sendilkumar Subramanian
  • Badrilal Mathur
  • Joseph Henry

Abstract

This paper presents a Wavelet packet transform with entropy features and support vector machine (SVM) based differential protection of power transformer by using internal fault and inrush current. The wavelet packet transform one of the powerful signal-processing tool and it is used to extract the information of differential current from third level using Db 9 mother wavelet. A two cycles of transformer fault current data is processed through wavelet packet transform to obtain wavelet coefficients and then features are extracted by using Shannon entropy principle. Subsequently, the extracted features are applied as inputs to SVM for distinguishing inrush current from internal fault. The application of this method is studied through detailed simulation of different faults on a power transformer using MATLAB/SIMULINK software. The results of the proposed new technique were found to be reliable, fast and accurate in identifying the fault condition.

Suggested Citation

  • Sendilkumar Subramanian & Badrilal Mathur & Joseph Henry, 2010. "Wavelet Packet Transform and Support Vector Machine Based Discrimination of Power Transformer Inrush Current from Internal Fault Currents," Modern Applied Science, Canadian Center of Science and Education, vol. 4(5), pages 1-67, May.
    • Handle: RePEc:ibn:masjnl:v:4:y:2010:i:5:p:67
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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