A COMPARATIVE ANALYSIS OF BOOST CONVERTER TOPOLOGIES FOR PHOTOVOLTAIC SYSTEMS USING MPPT (PO) AND BETA METHODS UNDER PARTIAL SHADING

Authors

  • SAMAH SEBA Laboratory of Materials, Energetic Systems, Renewable Energies and Energy Management, Amar Telidji University of Laghouat, Algeria Author
  • MOUHOUB BIRANE Laboratory of Materials, Energetic Systems, Renewable Energies and Energy Management, Amar Telidji University of Laghouat, Algeria Author
  • KHALIL BENMOUIZA Laboratory of Materials, Energetic Systems, Renewable Energies and Energy Management, Amar Telidji University of Laghouat, Algeria Author

DOI:

https://doi.org/10.59277/RRST-EE.2023.4.9

Keywords:

Photovoltaic (PV) system, Maximum Power Point Tracking (MPPT), Dc-dc converter, Topologies, Beta method

Abstract

Photovoltaic systems have become a popular renewable energy source due to their many benefits. Optimizing PV systems requires efficiently extracting the maximum power point. This can be achieved by optimizing maximum peak power tracking algorithms or testing different PV system topologies.  In this paper, different topologies of dc/dc converters with two MPPT algorithms (Beta and perturb and observe) are tested to control the duty ratio of the converters.  At first, the modeling of different DC/DC converter topologies to optimize the maximum power point tracking efficiency of photovoltaic systems is achieved. The simulation results show that the Beta algorithm gives a higher accuracy and efficiency than the P&O algorithm, especially in low oscillations with fast convergence speed. According to the findings, a parallel arrangement of boost converters is usually preferable to a series arrangement, especially for following changes in irradiance.

References

(1) A. Laib, F. Krim, B. Talbi, H. Feroura, A. Belaout, Hardware implementation of fuzzy maximum power point tracking through sliding mode current control for photovoltaic systems, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg, 66, 2, pp. 91–96(2022).

(2) M. Birane, C. Larbes, A. Cheknane, Comparative study and performance evaluation of central and distributed topologies of photovoltaic system, Int J Hydrogen Energy, 42, 13, pp. 8703–8711 (2017).

(3) S. Silvestre , A. Chouder, Effects of shadowing on photovoltaic module performance. Progress in Photovoltaics, Research and applications, 16, 2, pp.141-149(2008).

(4) J. C. Hernandez, O. G. Garcia, F. Jurado, Photovoltaic devices under partial shading conditions, International Review on Modelling and Simulations, 5, 1, pp. 414-425 (2012).

(5) A. Harrag, M. Hatti, Hardware in the loop experimental assessment of perturb and observe and IC state flow photovoltaic maximum power point tracking system, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg, 67, 3, pp. 287–292 (2022).

(6) N. Kacimi, A. Idir,S. Grouni, M.S. Boucherit, New combined method for tracking the global maximum power point of photovoltaic systems, Rev. Roum. Sci. Techn.–Électrotechn. Et Énerg., 67, 3, pp. 349–354, (2022).

(7) W. Xiao, A modified adaptive hill climbing maximum power point tracking (MPPT) control method for photovoltaic power systems, University of British Columbia (2003).

(8) M.A. Elgendy, B. Zahawi, D.J. Atkinson, Operating characteristics of the P&O algorithm at high perturbation frequencies for standalone PV systems, IEEE Transactions on Energy Conversion, 30, 1, pp. 189–198 (2015).

(9) M.A. Elgendy, B. Zahawi, D J. Atkinson, Assessment of the incremental conductance maximum power point tracking algorithm, IEEE Trans Sustain Energy, 4, 1, pp. 108–117 (2013).

(10) S. Jain, V. Agarwal, A new algorithm for rapid tracking of approximate maximum power point in photovoltaic systems, IEEE Power Electronics Letters, 2, 1, pp. 16–19 (2004).

(11) M. Zagrouba, A. Sellami, M. Bouaïcha, M. Ksouri, Identification of PV solar cells and modules parameters using the genetic algorithms: Application to maximum power extraction, Solar Energy, 84, 5, pp. 860–866 (2010).

(12) K. M. El-Naggar, M.R. AlRashidi, M.F. AlHajri, A. K. Al-Othman, Simulated annealing algorithm for photovoltaic parameters identification, Solar Energy, 86, 1, pp. 266–274 (2012).

(13) V. Khanna, B.K. Das, D. Bisht, Vandana, P.K. Singh, A three diode model for industrial solar cells and estimation of solar cell parameters using PSO algorithm, Renew Energy, 78, pp. 105–113 (2015).

(14) J.-S. Chou, D.-N. Truong, Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems, Chaos Solitons Fractals, 135, p. 109738 (2020).

(15) E.A. Gouda, M.F. Kotb, A.A. El-Fergany, Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis, Energy, 221, p. 119836 (2021).

(16) H. Tao, M. Ghahremani, F.W. Ahmed, W. Jing, M.S. Nazir, K. Ohshima, A novel MPPT controller in PV systems with hybrid whale optimization-PS algorithm based ANFIS under different conditions, Control Eng Pract, 112, p. 104809 (2021).

(17) W. Issaadi, S. Issaadi, Influence of the sampling frequency on various maximum power point tracking, Rev. Roum. Sci. Techn.–Électrotechn. Et Énerg., 68, 1, pp. 12-17(2023).

(18) M.R. Vincheh, A. Kargar, G. A. Markadeh, A hybrid control method for maximum power point tracking (MPPT) in photovoltaic systems, Arab J Sci Eng, 39, 6, pp. 4715–4725 (2014).

(19) A. Harrag, H. Bahri, Novel neural network IC-based variable step size fuel cell MPPT controller, Int J Hydrogen Energy, 42, 5, pp. 3549–3563 (2017).

(20) M. Birane, A. Derrouazin, M. Aillerie, A. Cheknane, C. Larbes, Evaluation and performance of different topologies of converters with efficient MPPT in a photovoltaic system, Journal of Electrical Systems, 16, 3, pp. 308–319 (2020).

(21) K. Benmouiza, Comparison analysis of different grid-connected PV systems topologies, Journal Europeen des Systemes Automatises, 55, 6, pp. 779–785 (2022).

(22) R. Bisht, A. Sikander, An improved method based on fuzzy logic with beta parameter for PV MPPT system, Optik (Stuttg), 259 (2022).

(23) R. Bisht and A. Sikander, A New soft computing-based parameter estimation of solar photovoltaic system, Arab J Sci Eng, 47, 3, pp. 3341–3353 (2022).

(24) W. Hayder, A. Abid, M. Ben Hamed, L. Sbita, Improved PSO algorithms in PV system optimisation, European Journal of Electrical Engineering and Computer Science, 4, 1, (2020).

(25) S. Saad, Enhancement of solar cell modeling with MPPT command practice with an electronic edge filter, Engineering, Technology & Applied Science Research, 11, 4, pp. 7501–7507 (2021).

(26) G. Sree Lakshmi, Dr. S. Harivardhagini, Fast-converging speed and zero oscillation MPPT method for PV system, CVR Journal of Science & Technology, 18, 1, pp. 62–70 (2020).

(27) X. Li, H. Wen, L. Jiang, W. Xiao, Y. Du, C. Zhao, An Improved MPPT Method for PV System with Fast-Converging Speed and Zero Oscillation, IEEE Trans Ind Appl, 52, 6, pp. 5051–5064 (2016).

(28) 28. X. Li, H. Wen, L. Jiang, E.G. Lim, Y. Du, C. Zhao, Photovoltaic modified β-parameter-based MPPT method with fast tracking, Journal of Power Electronics, 16, 1, pp. 9–17 (2016).

Downloads

Published

23.12.2023

Issue

Section

Électrotechnique et électroénergétique | Electrical and Power Engineering

How to Cite

A COMPARATIVE ANALYSIS OF BOOST CONVERTER TOPOLOGIES FOR PHOTOVOLTAIC SYSTEMS USING MPPT (PO) AND BETA METHODS UNDER PARTIAL SHADING. (2023). REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE, 68(4), 375-380. https://doi.org/10.59277/RRST-EE.2023.4.9