آمار

تعداد نشریات27
تعداد شماره‌ها364
تعداد مقالات3,223
تعداد مشاهده مقاله4,741,099
تعداد دریافت فایل اصل مقاله3,238,519
Bhasker, S. K., Tripathy, M., Agrawal, A., Mishra, A.. (1402). Differential Protection of ISPST Using Chebyshev Neural Network ‎. سامانه مدیریت نشریات علمی, 11(2), 123-129. doi: 10.22098/joape.2023.10004.1709
S. K. Bhasker; M. Tripathy; A. Agrawal; A. Mishra. "Differential Protection of ISPST Using Chebyshev Neural Network ‎". سامانه مدیریت نشریات علمی, 11, 2, 1402, 123-129. doi: 10.22098/joape.2023.10004.1709
Bhasker, S. K., Tripathy, M., Agrawal, A., Mishra, A.. (1402). 'Differential Protection of ISPST Using Chebyshev Neural Network ‎', سامانه مدیریت نشریات علمی, 11(2), pp. 123-129. doi: 10.22098/joape.2023.10004.1709
Bhasker, S. K., Tripathy, M., Agrawal, A., Mishra, A.. Differential Protection of ISPST Using Chebyshev Neural Network ‎. سامانه مدیریت نشریات علمی, 1402; 11(2): 123-129. doi: 10.22098/joape.2023.10004.1709

Differential Protection of ISPST Using Chebyshev Neural Network ‎

Journal of Operation and Automation in Power Engineering
دوره 11، شماره 2، آبان 2023، صفحه 123-129    اصل مقاله (746.59 K)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.10004.1709
نویسندگان
S. K. Bhasker* 1؛ M. Tripathy2؛ A. Agrawal3؛ A. Mishra3
1Department of Electrical Engineering, Faculty of Engineering & Technology, University of Lucknow, Lucknow, India
2Department of Electrical Engineering, Indian Institute of Technology, Roorkee, India‎
3Department of Electronics and Communication Engineering, BML Munjal University, Haryana, India‎
چکیده
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.
کلیدواژه‌ها
Energization؛ Internal Fault؛ Chebyshev Neural Network (ChNN)؛ ISPST؛ PSCAD/EMTDC.‎
مراجع
  1. IEEE Power Engineering society, “IEEE Standards C57.135, IEEE guide for the application, specification and testing of phase shifting transformer, 2002.
  2. Verboomen et al., “Phase shifting transformer: principles and applications”, Int. Conf. Future Power Syst., 2005.
  3. Khan and T. S. Sidhu, “Protection of standard-delta phase shifting transformer using terminal currents and voltages”, Electr. Power Syst. Res., vol. 110, pp. 31-8, 2014.
  4. A. Ibrahim and F. P. Stacom, “Phase angle regulating transformer protection”, IEEE Trans. Power Del., vol. 9, no. 1, pp. 394-404, 1994.
  5. IEEE Power System Relaying Committee, “Protection of Phase Angle Regulating Transformers”, 1999.
  6. Gajic, “Use of Standard 87T DP for special three-phase power transformers-part i: theory”, IEEE Trans. Power Del., vol. 27, no. 3, pp. 1035–1040, 2012.
  7. K. Bhasker and M. Tripathy, “Turn-to-Turn fault detection in indirect symmetrical PST based on negative sequence current components”, 5th IEEE Uttar Pradesh Sec. Int. Conf. Electr. Electron. Computer Eng., 2018.
  8. el-Ghany, I. A. Soliman, A. M. Azmy, “A reliable differential protection algorithm for delta hexagonal phase-shifting transformers”, Int. J. Electr. Power Energy Syst., vol. 127, 2021, 106671.
  9. IEEE Std. C37.91- 2008, IEEE Guide for Protecting Power Transformers. 2008.
  10. A. Soliman, H. A. Abd el-Ghany, A. M. Azmy, “A robust differential protection technique for single core delta-hexagonal phase-shifting transformers”, Int. J. Electr. Power Energy Syst., vol. 109, 2019, pp. 207-216.
  11. Khan and T. S. Sidhu, “New algorithm for the protection of delta-hexagonal phase shifting transformer”, IET Gener. Transm. Distrib., vol. 8, no. 1, pp. 178–186, 2014.
  12. M. Aboshady, “Modified distance protection for transmission line with hexagonal phase-shifting transformer”, Int. J. Electr. Power Energy Syst., vol. 134, 2022, 107379.
  13. Torkaman and V. Naeini, “Recognition and location of power transformer turn to turn fault by analysis of winding imposed forces”, J. Oper. Autom. Power Eng., vol. 7, no. 2, pp. 227-234, Oct. 2019.
  14. Behjat, A. Shams and V. Tamjidi, “Characterization of power transformer electromagnetic forces affected by winding faults”, J. Oper. Autom. Power Eng., vol. 6, no. 1, pp. 40-49, 2018.
  15. Tripathy, R. P. Maheshwari, and H. K. Verma, “Power transformer DP based on optimal probabilistic neural network”, IEEE Trans. Power Del., vol. 25, no. 1, pp. 102–112, 2010.
  16. M. Shah and B. R. Bhalja, “Discrimination between internal faults and other disturbances in transformer using the support vector machine-based protection scheme”, IEEE Trans. Power Del., vol. 28, no. 3, pp. 1508-15, 2013.
  17. Behkam et al., “Mechanical fault types detection in transformer windings using ‎interpretation of frequency responses via multilayer perceptron”, J. Oper. Autom. Power Eng., 2022.
  18. Balaga et al., “GA trained parallel hidden layered ANN based DP of three phase power transformer”, Int. J. Electr. Power Energy Syst., vol. 67, pp. 286–297, 2015.
  19. Jiao et al., “Transformer multi-feature intelligence protection principle based on BP neural network”, IEEE 4th Int. Electr. Energy Conf., 2021.
  20. Purwar, I. N. Kar, and A. N. Jha, “On-line system identification of complex systems using Chebyshev neural networks”, Appl. Soft Comput., vol. 7, no. 1, pp. 364-372, Jan. 2007.
  21. Vyas, B. Das, and R. P. Maheshwari, “An improved scheme for identifying fault zone in a series compensated transmission line using undecimated wavelet transform and Chebyshev Neural Network”, Int. J. Electr. Power Energy Syst., vol. 63, pp. 760-68, 2014.
  22. Zhang and J. Zhang, “Chebyshev functional link spline neural filter for nonlinear dynamic system identification”, IEEE Trans. Circuits Syst. II: Express Briefs, 2021.
  23. Dehuri and S.-B. Cho, “A comprehensive survey on functional link neural networks and an adaptive PSO-BP learning for CFLNN”, Neural Comput. Appl., vol. 19, no. 2, pp. 187–205, Jul. 2009.
  24. A. Tziouvaras, “Protecting a 138 KV phase shifting transformer: EMTP modeling and model power system testing”, Annual Georgia Tech. Protect. Relay. Conf., 2003.
  25. H. Horowitz and A. G. Phadke, Power System Relaying. 2008.
  26. Siemons Industry, “Current transformer relaying accuracies -IEEE compared to IEC”, 2013.
  27. P. Medeiros et al, “Performance assessment of a wavelet-based DP scheme in the inter-turn fault detection in a transformer”, Workshop Commun. Net. Power Syst., 2020.
  28. Hosny and V. K. Sood, “Transformer differential protection with phase angle difference based inrush restraint”, Electr. Power Syst. Res., vol. 115, pp. 57–64, Apr. 2014.
  29. N. Khan, “Modeling and protection of phase shifting transformers”, Doctoral Thesis, Department of electrical and computer engineering, The University of Western Ontario London, Ontario, 2013.
  30. Gajic, “Differential protection for arbitrary three-phase power transformers”, Doctoral Thesis, Department of industrial electrical engineering and automation, Lund University, Sweden, 2008.
آمار
تعداد مشاهده مقاله: 878
تعداد دریافت فایل اصل مقاله: 665


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب