Revista UIS Ingenierías

Vol. 21 No. 2 (2022): Revista UIS Ingenierías
Articles

Application of salp swarm optimization algorithm to estimate parameters in single-phase transformers considering voltage and current measures

PDF (Español (España))
  • Laura Sofía Avellaneda-Gómez,
  • Oscar Danilo Montoya-Giraldo
Laura Sofía Avellaneda-Gómez
Universidad Distrital Francisco José de Caldas
Oscar Danilo Montoya-Giraldo
Universidad Distrital Francisco José de Caldas
DOI: https://doi.org/10.18273/revuin.v21n2-2022011

Published 2022-05-05

Keywords

  • parametric estimation in transformers,
  • equivalent circuit,
  • single-phase transformers,
  • nonlinear programming model,
  • Salp Swarm optimization algorithm,
  • voltage and current measures,
  • mean square error,
  • metaheuristic optimization,
  • comparison techniques,
  • numerical performance
    • ...More
    Less

How to Cite

Avellaneda-Gómez, L. S., & Montoya-Giraldo, O. D. (2022). Application of salp swarm optimization algorithm to estimate parameters in single-phase transformers considering voltage and current measures. Revista UIS Ingenierías, 21(2), 131–146. https://doi.org/10.18273/revuin.v21n2-2022011

Copyright (c) 2022 Revista UIS Ingenierías

Creative Commons License

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Abstract

This article presents a solution methodology for the estimation of parameters of single-phase transformers considering the measurements of voltage and current, for which a non-linear optimization model is used. This model is based on minimizing the mean square error between the measured and calculated voltage and current variables. This nonlinear programming model is solved by implementing the Salp swarm optimization algorithm. The results obtained show that the proposed optimization method allows reducing the error between the estimation of the measured and calculated variables; in addition, the proposed optimization method improves the results presented by other optimization methods reported in the specialized literature. All the simulations were performed in the MATLAB programming environment.

PDF (Español (España))

Downloads

Download data is not yet available.

References

  1. T. R. Chaves, M. A. I. Martins, K. A. Martins, A. F. de Macedo, and S. de Francisci, “Application Study in the Field of Solutions for the Monitoring Distribution Transformers of the Overhead Power Grid,” Energies, vol. 14, no. 19, p. 6072, 2021, doi: https://doi.org/10.3390/en14196072
  2. S. Guarín, S. Velarde, E. Castaño, A. Molina-Cabrera, “Construction and simulation of a planar transformer prototype,” Transactions on Energy Systems and Engineering Applications, vol. 2, no. 2, pp. 1-7, 2021, doi: https://doi.org/10.32397/tesea.vol2.n2.1
  3. H. Besharatifard, S. Hasanzadeh, E. Heydarian Forushani, H. H. Alhelou, P. Siano, “Detection and Analysis of Partial Discharges in OilImmersed Power Transformers Using Low Cost Acoustic Sensors,” Applied Sciences, vol. 12, no. 6, p. 3010, 2022, doi: https://doi.org/10.3390/app12063010
  4. S. Bhattacharya, “Transforming the transformer,” IEEE Spectrum, vol. 54, no. 7, pp. 38-43, 2017, doi: https://doi.org/10.1109/MSPEC.2017.7951721
  5. M. Tahir, S. Tenbohlen, “A Comprehensive Analysis of Windings Electrical and Mechanical Faults Using a High Frequency Model,” Energies, vol. 13, no. 1, p. 105, 2019, doi: https://doi.org/10.3390/en13010105
  6. J. Gao, J. Gao, Q. Guo, L. Li, “Research on operation status and fault deduction system design of transformer in large sports venues,” Energy Reports, vol. 8, pp. 539-546, 2022, doi: https://doi.org/10.3390/en13010105
  7. B. Zeng, J. Guo, W. Zhu, Z. Xiao, F. Yuan, S. Huang, “A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM,” Energies, vol. 12, no. 21, p. 4170, 2019, doi: https://doi.org/10.3390/en12214170
  8. S. Y. Bocanegra, O. D. Montoya, A. Molina-Cabrera, “Estimación de parámetros en transformadores monofásicos empleando medidas de tensión y corriente,” Revista UIS Ingenierías, vol. 19, no. 4, pp. 63-75, may 2020, doi: https://doi.org/10.18273/revuin.v19n4-2020006
  9. S. Y. Bocanegra, O. D. Montoya, A. Molina, “Sine-cosine optimization approach applied to the parametric estimation in single phase transformers by considering voltage and current measures,” Dyna, vol. 88, no. 219, pp. 19-27, 2021, doi: https://doi.org/10.15446/dyna.v88n219.93670
  10. A. Tokić, I. Uglešić, G. Štumberger, “Simulations of Transformer Inrush Current by Using BDF-Based Numerical Methods,” Mathematical Problems in Engineering, vol. 2013, pp. 1-10, 2013, doi: https://doi.org/10.1155/2013/215647
  11. M. Ćalasan, D. Mujičić, V. Rubežić, M. Radulović, “Estimation of Equivalent Circuit Parameters of Single-Phase Transformer by Using Chaotic Optimization Approach,” Energies, vol. 12, no. 9, p. 1697, 2019, doi: https://doi.org/10.1155/2013/215647
  12. M. I. Abdelwanis, A. Abaza, R. A. El-Sehiemy, M. N. Ibrahim, H. Rezk, “Parameter Estimation of Electric Power Transformers Using Coyote Optimization Algorithm With Experimental Verification,” IEEE Access, vol. 8, pp. 50 036-50 044, 2020, doi: https://doi.org/10.1109/ACCESS.2020.2978398
  13. R. Kazemi, S. Jazebi, D. Deswal, F. de Leon, “Estimation of Design Parameters of SinglePhase Distribution Transformers From Terminal Measurements,” IEEE Transactions on Power Delivery, vol. 32, no. 4, pp. 2031-2039, 2017, doi: https://doi.org/10.1109/TPWRD.2016.2621753
  14. M. Calasan, A. Jovanovic, V. Rubezic, D. Mujicic,
  15. A. Deriszadeh, “Notes on parameter estimation for single-phase transformer,” IEEE Transactions on Industry Applications, pp. 1-1, 2020, doi: https://doi.org/10.1109/TIA.2020.2992667
  16. S. Padma, S. Subramanian, “Parameter estimation of single phase core type transformer using bacterial foraging algorithm,” Engineering, vol. 2, no. 11, p. 917, 2010, doi: http://dx.doi.org/10.4236/eng.2010.211115
  17. M. I. Mossad, M. Azab, A. Abu-Siada, “Transformer parameters estimation from nameplate data using evolutionary programming techniques,” IEEE transactions on power delivery, vol. 29, no. 5, pp. 2118-2123, 2014, doi: https://doi.org/10.1109/TPWRD.2014.2311153
  18. D. Bhowmick, M. Manna, S. K. Chowdhury, “Estimation of equivalent circuit parameters of transformer and induction motor from load data,” IEEE Transactions on Industry Applications, vol. 54, no. 3, pp. 2784-2791, 2018, doi: https://doi.org/10.1109/TIA.2018.2790378
  19. Z. Yilmaz, M. Oksar, F. Basciftci, “Multiobjective artificial bee colony algorithm to estimate transformer equivalent circuit parameters,” Periodicals of Engineering and Natural Sciences, vol. 5, no. 3, 2017, doi: http://dx.doi.org/10.21533/pen.v5i3.103
  20. C. A. Arenas-Acuña, J. A. Rodríguez-Contreras, O. D. Montoya, E. Rivas-Trujillo, “Blackhole optimization applied to the parametric estimation in distribution transformers considering voltage and current measures,” Computers, vol. 10, no. 10, p. 124, 2021, doi: https://doi.org/10.3390/computers10100124
  21. H. A. Illias, K. Mou, A. Bakar, “Estimation of transformer parameters from nameplate data by imperialist competitive and gravitational search algorithms,” Swarm and Evolutionary Computation, vol. 36, pp. 18-26, 2017, doi: https://doi.org/10.1016/j.swevo.2017.03.003
  22. D. G. Gracía-Velásquez, A. S. Morales-Rodríguez, O. D. Montoya, “Application of the Crow Search Algorithm to the Problem of the Parametric Estimation in Transformers Considering Voltage and Current Measures,” Computers, vol. 11, no. 1, p. 9, 2022, doi: https://doi.org/10.3390/computers11010009
  23. S. J. Chapman, Electric machinery fundamentals.
  24. McGraw-Hill, 2004.
  25. S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, S. M. Mirjalili, “Salp swarm algorithm: A bio-inspired optimizer for engineering design problems,” Advances in Engineering Software, vol. 114, pp. 163-191, 2017, doi: https://doi.org/10.1016/j.advengsoft.2017.07.002
  26. A. E. Hegazy, M. Makhlouf, G. S. El-Tawel, “Improved salp swarm algorithm for feature selection,” Journal of King Saud University – Computer and Information Sciences, vol. 32, no. 3, pp. 335-344, 2020, doi: https://doi.org/10.1016/j.jksuci.2018.06.003
  27. R. A. Ibrahim, A. A. Ewees, D. Oliva, M. Abd Elaziz, and S. Lu, “Improved salp swarm algorithm based on particle swarm optimization for feature selection,” Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 8, pp. 3155-3169, 2019, doi: https://doi.org/10.1007/s12652-018-1031-9
  28. H. Zhang, T. Liu, X. Ye, A. A. Heidari, G. Liang, H. Chen, and Z. Pan, “Differential evolutionassisted salp swarm algorithm with chaotic structure for real-world problems,” Engineering with Computers, 2022, doi: https://doi.org/10.1007/s00366-021-01545-x
  29. T. A. Jumani, M. Mustafa, W. Anjum, S. Ayub et al., “Salp swarm optimization algorithmbased controller for dynamic response and power quality enhancement of an islanded microgrid,” Processes, vol. 7, no. 11, p. 840, 2019, doi: https://doi.org/10.3390/pr7110840
  30. J. Montano, A. F. T. Mejía, A. A. Rosales Muñoz, F. Andrade, O. D. Garzín Rivera, J. M. Palomeque, “Salp swarm optimization algorithm for estimating the parameters of photovoltaic panels based on the three-diode model,” Electronics, vol. 10, no. 24, p. 3123, 2021, doi: https://doi.org/10.3390/electronics10243123
  31. S. B. Chaabane, A. Belazi, S. Kharbech, A. Bouallegue, L. Clavier, “Improved Salp Swarm Optimization Algorithm: Application in Feature Weighting for Blind Modulation Identification,” Electronics, vol. 10, no. 16, p. 2002, 2021, doi: https://doi.org/10.3390/electronics10162002
  32. S. Baqaruzi and S. T. Kasim, “Comparison of effect
  33. efficiency and voltage regulation between three-phase transformer winding connections,” Bulletin of Computer Science and Electrical Engineering, vol. 1, no. 2, pp. 54–62, 2020, doi: https://doi.org/10.25008/bcsee.v1i2.1123