University of Mohaghegh Ardabili Journal of Operation and Automation in Power Engineering 2322-4576 11 2 2023 08 01 Differential Protection of ISPST Using Chebyshev Neural Network ‎ حفاظت افتراقی ISPST با استفاده از شبکه عصبی چبیشف 123 129 1598 10.22098/joape.2023.10004.1709 EN S. K. Bhasker Department of Electrical Engineering, Faculty of Engineering & Technology, University of Lucknow, Lucknow, India M. Tripathy Department of Electrical Engineering, Indian Institute of Technology, Roorkee, India‎ A. Agrawal Department of Electronics and Communication Engineering, BML Munjal University, Haryana, India‎ A. Mishra Department of Electronics and Communication Engineering, BML Munjal University, Haryana, India‎ Journal Article 2021 12 16 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. https://joape.uma.ac.ir/article_1598_b411d0dcf8b92f3928d0f9c02ac84c8d.pdf