TY - JOUR ID - 1598 TI - Differential Protection of ISPST Using Chebyshev Neural Network ‎ JO - Journal of Operation and Automation in Power Engineering JA - JOAPE LA - en SN - 2322-4576 AU - Bhasker, S. K. AU - Tripathy, M. AU - Agrawal, A. AU - Mishra, A. AD - Department of Electrical Engineering, Faculty of Engineering & Technology, University of Lucknow, Lucknow, India AD - Department of Electrical Engineering, Indian Institute of Technology, Roorkee, India‌ AD - Department of Electronics and Communication Engineering, BML Munjal University, Haryana, India‌ Y1 - 2023 PY - 2023 VL - 11 IS - 2 SP - 123 EP - 129 KW - Energization KW - Internal Fault KW - Chebyshev Neural Network (ChNN) KW - ISPST KW - PSCAD/EMTDC.‎ DO - 10.22098/joape.2023.10004.1709 N2 - 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. UR - https://joape.uma.ac.ir/article_1598.html L1 - https://joape.uma.ac.ir/article_1598_b411d0dcf8b92f3928d0f9c02ac84c8d.pdf ER -