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A New Soft Computing-Based Parameter Estimation of Solar Photovoltaic System

  • Research Article-Electrical Engineering
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Abstract

The rapid increment in energy demand has resulted in advancement in the design of solar PV system and maximum power point tracking (MPPT) techniques. For estimation of MPP, the actual characteristics of solar panels are to be determined. To obtain the actual characteristics of solar panel, its electrical parameters such as photon current (\(I_\mathrm{P H}\)), saturation current (\(I_\mathrm{O}\)), series resistance (\(R_\mathrm{SE}\)), shunt resistance (\(R_\mathrm{SH}\)), and thermal voltage (\(V_\mathrm{T}\)) need to be obtained. This paper first discusses the mathematical model of single-diode PV cell in terms of two parameters. A new algorithm based on sanitized teacher learning-based optimization (s-TLBO) is used to estimate the parameters using a defined fitness function. In the end, PV characteristics and relative error for different solar panels are estimated using the proposed algorithm, and the results are compared with existing algorithms. It was concluded that the proposed algorithm provides PV characteristics very close to actual characteristics.

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References

  1. Tina, G.: A coupled electrical and thermal model for photovoltaic modules. Trans. ASME J. Sol. Energy Eng. 132(2), 0245011–0245015 (2010). https://doi.org/10.1115/1.4001149.

    Article  Google Scholar 

  2. Chan, D.S.; Phang, J.C.: Analytical methods for the extraction of solar-cell single-and double-diode model parameters from I–V characteristics. IEEE Trans. Electron Dev. 34(2), 286–293 (1987). https://doi.org/10.1109/T-ED.1987.22920.

    Article  Google Scholar 

  3. Phang, J.C.; Chan, D.S.; Phillips, J.R.: Accurate analytical method for the extraction of solar cell model parameters. Electron. Lett. 20(10), 406–408 (1984). https://doi.org/10.1049/el:19840281.

    Article  Google Scholar 

  4. Silva, E.A.; Bradaschia, F.; Cavalcanti, M.C.; Nascimento, A.J.: Parameter estimation method to improve the accuracy of photovoltaic electrical model. IEEE J. Photovolt. 6(1), 278–285 (2016). https://doi.org/10.1109/JPHOTOV.2015.2483369.

    Article  Google Scholar 

  5. Jordehi, A.R.: Parameter estimation of solar photovoltaic (PV) cells: a review. Renew. Sustain. Energy Rev. 61, 354–371 (2016). https://doi.org/10.1016/j.rser.2016.03.049.

    Article  Google Scholar 

  6. Chatterjee, A.; Keyhani, A.; Kapoor, D.: Identification of photovoltaic source models. IEEE Trans. Energy Convers. 26(3), 883–889 (2011). https://doi.org/10.1109/TEC.2011.2159268.

    Article  Google Scholar 

  7. Shongwe, S.; Hanif, M.: Comparative analysis of different single-diode PV modeling methods. IEEE J. Photovolt. 5(3), 938–946 (2015). https://doi.org/10.1109/JPHOTOV.2015.2395137.

    Article  Google Scholar 

  8. Koksal, M.E.; Senol, M.; Unver, A.K.: Numerical simulation of power transmission lines. Chin. J. Phys. 59(January), 507–524 (2019). https://doi.org/10.1016/j.cjph.2019.04.006.

    Article  MathSciNet  Google Scholar 

  9. Koksal, M.E.: Time and frequency responses of non-integer order RLC circuits. AIMS Math. 4(1), 64–78 (2019). https://doi.org/10.3934/Math.2019.1.64.

    Article  MathSciNet  MATH  Google Scholar 

  10. Villalva, M.G.; Gazoli, J.R.; Filho, E.R.: Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans. Power Electron. 24(5), 1198–1208 (2009). https://doi.org/10.1109/TPEL.2009.2013862.

    Article  Google Scholar 

  11. Uoya, M.; Koizumi, H.: A calculation method of photovoltaic array’s operating point for MPPT evaluation based on one-dimensional Newton–Raphson method. IEEE Trans. Ind. Appl. 51(1), 567–575 (2015). https://doi.org/10.1109/TIA.2014.2326083.

  12. Yetayew, T.T., Jyothsna, T.R.: Parameter extraction of photovoltaic modules using Newton Raphson and simulated annealing techniques. In: 2015 IEEE Power, Communication and Information Technology Conference, PCITC 2015—Proceedings, pp. 229–234 (2016). https://doi.org/10.1109/PCITC.2015.7438166

  13. Abdulrazzaq, A.K.; Bognár, G.; Plesz, B.: Accurate method for PV solar cells and modules parameters extraction using I–V curves. J. King Saud Univ. Eng. Sci. (2020). https://doi.org/10.1016/j.jksues.2020.07.008.

    Article  Google Scholar 

  14. Islas, M.A.; Rubio, J.d.J.; Muñiz, S.; Ochoa, G.; Pacheco, J.; Meda-Campaña, J.A.; Mujica-Vargas, D.; Aguilar-Ibañez, C.; Gutierrez, G.J.; Zacarias, A.: A fuzzy logic model for hourly electrical power demand modeling. Electronics (Switzerland) 10(4), 1–12 (2021). https://doi.org/10.3390/electronics10040448

  15. Rubio, J.D.J.: SOFMLS: Online self-organizing fuzzy modified least-squares network. IEEE Trans. Fuzzy Syst. 17(6), 1296–1309 (2009). https://doi.org/10.1109/TFUZZ.2009.2029569.

    Article  Google Scholar 

  16. Rubio, J.d.J.: Stability analysis of the modified Levenberg-Marquardt algorithm. IEEE Trans. Neural Netw. Learn. Syst. 9, 1–15 (2020)

  17. Zagrouba, M.; Sellami, A.; Bouaïcha, M.; Ksouri, M.: Identification of PV solar cells and modules parameters using the genetic algorithms: application to maximum power extraction. Sol. Energy 84(5), 860–866 (2010). https://doi.org/10.1016/j.solener.2010.02.012.

    Article  Google Scholar 

  18. El-Naggar, K.M.; AlRashidi, M.R.; AlHajri, M.F.; Al-Othman, A.K.: Simulated Annealing algorithm for photovoltaic parameters identification. Sol. Energy 86(1), 266–274 (2012). https://doi.org/10.1016/j.solener.2011.09.032.

    Article  Google Scholar 

  19. Wang, H.; Shen, J.: An improved model combining evolutionary algorithm and neural networks for PV maximum power point tracking. IEEE Access 7(1), 2823–2827 (2019). https://doi.org/10.1109/ACCESS.2018.2881888.

    Article  Google Scholar 

  20. Khanna, V., Das, B.K., Bisht, D., Vandana, Singh, P.K.: A three diode model for industrial solar cells and estimation of solar cell parameters using PSO algorithm. Renew. Energy 78, 105–113 (2015). https://doi.org/10.1016/j.renene.2014.12.072

  21. Nayak, B.; Mohapatra, A.; Mohanty, K.B.: Parameter estimation of single diode PV module based on GWO algorithm. Renew. Energy Focus 30(00), 1–12 (2019). https://doi.org/10.1016/j.ref.2019.04.003.

    Article  Google Scholar 

  22. Shankar, N.; Saravanakumar, N.; Indu Rani, B.: Solar photovoltaic module parameter estimation with an enhanced differential evolutionary algorithm using the manufacturer’s datasheet information. Optik 224, 165700 (2020)

  23. Mostafa, M.; Rezk, H.; Aly, M.; Ahmed, E.M.: A new strategy based on slime mould algorithm to extract the optimal model parameters of solar PV panel. Sustain. Energy Technol. Assessm. 42(November), 100849 (2020)

  24. Chiang, H.S.; Chen, M.Y.; Huang, Y.J.: Wavelet-based EEG processing for epilepsy detection using fuzzy entropy and associative petri net. IEEE Access 7, 103255–103262 (2019). https://doi.org/10.1109/ACCESS.2019.2929266.

    Article  Google Scholar 

  25. Meda-Campaña, J.A.: On the estimation and control of nonlinear systems with parametric uncertainties and noisy outputs. IEEE Access 6, 31968–31973 (2018). https://doi.org/10.1109/ACCESS.2018.2846483.

    Article  Google Scholar 

  26. Furlán, F.; Rubio, E.; Sossa, H.; Ponce, V.: CNN based detectors on planetary environments: a performance evaluation. Front. Neurorobot. 14(October), 1–9 (2020). https://doi.org/10.3389/fnbot.2020.590371.

    Article  Google Scholar 

  27. Ram, J.P.; Babu, T.S.; Dragicevic, T.; Rajasekar, N.: A new hybrid bee pollinator flower pollination algorithm for solar PV parameter estimation. Energy Convers. Manag. 135, 463–476 (2017). https://doi.org/10.1016/j.enconman.2016.12.082.

    Article  Google Scholar 

  28. AlRashidi, M.R.; AlHajri, M.F.; El-Naggar, K.M.; Al-Othman, A.K.: A new estimation approach for determining the I–V characteristics of solar cells. Sol. Energy 85(7), 1543–1550 (2011). https://doi.org/10.1016/j.solener.2011.04.013.

    Article  Google Scholar 

  29. Askarzadeh, A.; Rezazadeh, A.: Parameter identification for solar cell models using harmony search-based algorithms. Sol. Energy 86(11), 3241–3249 (2012). https://doi.org/10.1016/j.solener.2012.08.018.

    Article  Google Scholar 

  30. Kumar, C.; Raj, T.D.; Premkumar, M.; Raj, T.D.: A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters. Optik 223, 165277 (2020)

  31. Breitenstein, O.: An alternative one-diode model for illuminated solar cells. Energy Proc. 55, 30–37 (2014). https://doi.org/10.1016/j.egypro.2014.08.024.

    Article  Google Scholar 

  32. Xiao, W.; Edwin, F.F.; Spagnuolo, G.; Jatskevich, J.: Efficient approaches for modeling and simulating photovoltaic power systems. IEEE J. Photovolt. 3(1), 500–508 (2013). https://doi.org/10.1109/JPHOTOV.2012.2226435.

    Article  Google Scholar 

  33. Jadli, U.; Thakur, P.; Shukla, R.D.: A new parameter estimation method of solar photovoltaic. IEEE J. Photovolt. 8(1), 239–247 (2018). https://doi.org/10.1109/JPHOTOV.2017.2767602.

    Article  Google Scholar 

  34. Kirkpatrick, S.; Gelatt, C.D.; Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983). https://doi.org/10.1126/science.220.4598.671.

    Article  MathSciNet  MATH  Google Scholar 

  35. Mughal, M.A.; Ma, Q.; Xiao, C.: Photovoltaic cell parameter estimation using hybrid particle swarm optimization and simulated annealing. Energies 10(8), 1–13 (2017). https://doi.org/10.3390/en10081213.

    Article  Google Scholar 

  36. Ebrahimi, S.M.; Salahshour, E.; Malekzadeh, M.; Gordillo, F.: Parameters identification of PV solar cells and modules using flexible particle swarm optimization algorithm. Energy 179, 358–372 (2019). https://doi.org/10.1016/j.energy.2019.04.218.

    Article  Google Scholar 

  37. Chauhan, S.S.; Sivadurgaprasad, C.; Kadambur, R.; Kotecha, P.: A novel strategy for the combinatorial production planning problem using integer variables and performance evaluation of recent optimization algorithms. Swarm Evolution. Comput. 43(March), 225–243 (2018). https://doi.org/10.1016/j.swevo.2018.04.004.

    Article  Google Scholar 

  38. Rao, R.V.; Savsani, V.J.; Vakharia, D.P.: Computer-aided design teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Aided Des. 43(3), 303–315 (2011). https://doi.org/10.1016/j.cad.2010.12.015.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Rahul Bisht & Afzal Sikander

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  1. Rahul Bisht
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  2. Afzal Sikander
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Correspondence to Rahul Bisht.

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Bisht, R., Sikander, A. A New Soft Computing-Based Parameter Estimation of Solar Photovoltaic System. Arab J Sci Eng 47, 3341–3353 (2022). https://doi.org/10.1007/s13369-021-06209-y

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