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Neural Network based Modeling and Simulation for Estimation of Maximum Transformer Inrush Current

by Puneet Kumar Singh, D.K. Chaturvedi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 5
Year of Publication: 2015
Authors: Puneet Kumar Singh, D.K. Chaturvedi
10.5120/ijca2015905331

Puneet Kumar Singh, D.K. Chaturvedi . Neural Network based Modeling and Simulation for Estimation of Maximum Transformer Inrush Current. International Journal of Computer Applications. 123, 5 ( August 2015), 35-40. DOI=10.5120/ijca2015905331

@article{ 10.5120/ijca2015905331,
author = { Puneet Kumar Singh, D.K. Chaturvedi },
title = { Neural Network based Modeling and Simulation for Estimation of Maximum Transformer Inrush Current },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 5 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 35-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number5/21958-2015905331/ },
doi = { 10.5120/ijca2015905331 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:11:53.364868+05:30
%A Puneet Kumar Singh
%A D.K. Chaturvedi
%T Neural Network based Modeling and Simulation for Estimation of Maximum Transformer Inrush Current
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 5
%P 35-40
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Inrush current is a very important phenomenon which occurs in transformer during energization at no load. It depends on several factors like magnetizing properties of magnetic material, resistant and inductance of primary winding, supply frequency, switching angle of circuit breaker, applied voltage etc. Objective of this study is to estimate the maximum value of transformer inrush current. Measurement of inrush current is difficult issue for large rating of transformer under different switching conditions. Therefore this study is used to demonstrate Artificial Neural Network (ANN) for modeling and simulation. This helps in prediction of maximum inrush current for large rating of transformer. Back Propagation with Levenberg–Marquardt (LM) algorithm was used to train the ANN architecture and same was tested for the various data sets. For effective training of ANN various operating conditions are considered. These are Transformer VA rating, Applied Voltage, switching angle and Remnant flux. This study was carried out to develop ANN model in a generalize manner which can be utilize for predicting maximum value of current and harmonic for any single phase transformer provide core material and frequency remains same.

References
  1. L. F. Blume, G. Camilli, S. B. Farnham, and H. A. Peterson, ‘‘Transformer magnetizing inrush currents and influence on system operation,” AIEE Trans. Power App. Sys f. , vol. 63, pp. 366-375, Jan. 1944.
  2. E. 0. Schweitzer, R. R. Larson, and A. J . Flechsig, Jr., “An efficient inrush current detection algorithm for digital computer Proc”. IEEE,” in Power Eng. Sue. Summer Meet. (Mexico), pt. A77, pp. 510, July 1977
  3. R. Yacamini and A. Abu-Nasser, “Numerical calculation of inrush current in single phase transformers.” Proc. Inst. Elec. Eng., vol. 128, pt. B, no. 6, pp. 327-334, Nov. 1981.
  4. R. Yacamini and A. Abu-Nasser, , “The calculation of inrush current in three phase transformers,” Proc. Inst. Eke. Eng., vol. 133, pt. B, no. I, pp 31-40, Jan. 1986
  5. C Paul. Y. Ling and Amitava Basak. “Investigation of Magnetizing Inrush Current in a Single-phase Transformer.” IEEE transactions on magnetics. vol 24. no 6, pp 3217 – 3222, 1988.
  6. R. Yacamini and H. Bronzeado, “Transformer inrush calculations using a coupled electromagnetic model,” Proc. Inst. Elect. Eng., Sci., Meas. Technol., vol. 141, no. 6, pp. 491–498, Nov. 1994.
  7. Stringer, N.T., Lawhead, L,; Wilkerson, T.; Biggs, J., Rockefeller G.D. “Real-time transient testing and performance of transformer differential relays” IEEE, Volume: 2, 8-12, pp1142 – 1150, Oct. 1995.
  8. K. Yabe, “Power differential method for discrimination between fault and magnetizing inrush current in transformers,” IEEE Trans. Power Del., vol. 12, no. 3, pp. 1109–1118, Jul. 1997.
  9. Stringer, N.T., Lawhead, L., Wilkerson, T., Biggs, J., Rockefeller, G.D. “ Testing and performance of transformer differential relays” IEEE, Volume: 3, Issue: 4, pp:36 – 420, July-Aug. 1997.
  10. Z Q Bo, R K Aggarwal. A T Johns, “A new relaying principle for power transformer protection using transient comparison technique,” Developments in Power System Protection, pp. 139-142, 25-27th March 1997.
  11. K. S. Smith, L. Ran, and B. Leyman, “Analysis of transformer inrush transient in offshore electrical systems,” Proc. Inst. Elect. Eng., Gen.,Transm. Distrib., vol. 146, no. 1, pp. 89–95, Jan. 1999.
  12. J. J. Rico, E. Acha, and M. Madrigal, “The study of inrush current phenomenon using operational matrices,” IEEE Trans. Power Del., vol. 16, no. 2, pp. 231–237, Apr. 2001.
  13. Guzman, A.; Zocholl, S.; Benmouyal, G.; Altuve, H.J.,"a current-based solution for transformer differential protection-part I: Problem Statement", IEEE Transactions on Power Delivery, Volume 16, pp 485-491, October 2001.
  14. Saied, M.M., "A study on the inrush current phenomena in transformer substations", IEEE Volume: 2, vol.2, 30, pp1180 – 1187, Sept.-4 Oct. 2001.
  15. P. L. Mao and R. K. Aggarwal, “A novel approach to the classification of the transient phenomena in power transformers using combined wavelet transform and neural network,” IEEE Trans. Power Del., vol. 16, no. 4, pp. 654–660, , Oct. 2001.
  16. Guzman, A.; Zocholl, S.; Benmouyal, G.; Altuve, H.J.,"a current-based solution for transformer differential protection-part II: relay description and evaluation", IEEE Transactions on Power Delivery Volume 17, pp 886-893, ,October 2002.
  17. Controlled Switching of HVAC CBs—Planning, Specifications & Testing, CIGRÉ SC A3 Working Group A3.07, Jan. 2004.
  18. Andreas Ebner, Michael Bösch, Renato Cortesi. “Controlled Switching of Transformers – Effects of Closing Time Scatter and Residual Flux Uncertainty.” IEEE: Universities Power Engineering Conference.. 43rd . pp 1-5, 2008.
  19. Yunfei Wang, Sami G. Abdulsalam, and Wilsun Xu, “Analytical Formula to Estimate the Maximum Inrush Current”, IEEE Transactions on Power Delivery, VOL. 23, NO. 2, pp 1266-68, April 2008.
  20. M. G. Vanti, S. L. Bertoli, S. H. L. Cabral, A. G. Gerent, Jr., and P. Kuo-Peng, “Semianalytic Solution for a Simple Model of Inrush Currents in Transformers” IEEE Transactions on Magnetics, VOL. 44, NO. 6, June 2008.
  21. N. Chiesa and H. K. Høidalen, “Systematic switching study of transformer inrush current: Simulation and measurements,” presented at the Int. Conf. Power System Transients, Kyoto, Japan, Jun. 2009.
  22. M. Jamali, M. Mirzaie, S. Asghar Gholamian. “Calculation and Analysis of Transformer Inrush Current Based on Parameters of Transformer and Operating Conditions,” Electronics and Electrical Engineering. , Kaunas: Technologija, No. 3(109). pp. 17–20, 2011.
  23. Singh P K and D K chaturvedi “Modeling and Simulation of Single Phase Transformer Inrush Current using Neural Netwrok” National Conference ETEIC-2012 Proceedings, pp 296-299, April ,2012.
  24. Puneet Kumar Singh, D K Chaturvedi, "Neural Network based Modeling and Simulation of Transformer Inrush Current", IJISA, vol.4, no.5, pp.1-7, 2012.
  25. Yousefizadeh, H. and Zilouchian, A. Neural network structures. In Zilouchian A and Jamshidi M, editors. Intelligent control systems using soft computing methodologies. Boca Raton, FL: CRC Press, 39–66, 2001.
  26. Zilouchian, A. Fundamentals of neural networks. In Zilouchian A and Jamshidi M, editors. Intelligent control systems using soft computing methodologies. Boca Raton, FL: CRC Press, 17–38, 2001.
  27. Chaturvedi D. K. “Soft Computing Techniques and its Applications in Electrical Engineering” Springer, Vol 103, 2008.
  28. Yegnanarayana, B. Artificial Neural Networks. Prentice Hall of India, 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Transformer Inrush Current ANN Modeling Simulation Harmonic.

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