Differential Protection of ISPST Using Chebyshev Neural Network ‎

Document Type : Research paper

Authors

1 Department of Electrical Engineering, Faculty of Engineering & Technology, University of Lucknow, Lucknow, India

2 Department of Electrical Engineering, Indian Institute of Technology, Roorkee, India‎

3 Department of Electronics and Communication Engineering, BML Munjal University, Haryana, India‎

Abstract

An Indirect Symmetrical Phase Shift Transformer (ISPST) represents both electrically connected and magnetically coupled circuits, which makes it unique compared to a power transformer. Effective differentiation between transformer inrush current and internal fault current is necessary to avoid incorrect differential relay tripping. This research proposes a system that uses a Chebyshev Neural Network (ChNN) as a core classifier to distinguish such internal faults. For simulations, we used PSCAD/EMTDC software. Internal faults and inrush have been simulated in various ways using various ISPST parameters. A large, simulated dataset is used, and performance is recorded against different sized ISPSTs. We observed an overall accuracy greater than 99%. The ChNN classifier generated exceptionally favorable results even in case of noisy signal, CT saturation, and different ISPST parameters.

Keywords

References

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Journal of Operation and Automation in Power Engineering
Volume 11, Issue 2
August 2023
Pages 123-129

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History

  • Receive Date: 16 December 2021
  • Revise Date: 13 February 2022
  • Accept Date: 09 April 2022
  • First Publish Date: 23 April 2022

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