Differential Protection of ISPST Using Chebyshev Neural Network
عنوان مقاله: Differential Protection of ISPST Using Chebyshev Neural Network
شناسه ملی مقاله: JR_JOAPE-11-2_006
منتشر شده در در سال 1402
شناسه ملی مقاله: JR_JOAPE-11-2_006
منتشر شده در در سال 1402
مشخصات نویسندگان مقاله:
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
خلاصه مقاله:
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
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 ۹۹%. The ChNN classifier generated exceptionally favorable results even in case of noisy signal, CT saturation, and different ISPST parameters.
کلمات کلیدی: Energization, Internal Fault, Chebyshev Neural Network (ChNN), ISPST, PSCAD/EMTDC.
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1810998/