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Optimization of an Iterative Approach for Precise Identification of Photovoltaic Cell Parameters

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Abstract:

The goal of this study is to employ an optimization algorithm to estimate the unknown electrical parameters of equivalent cell circuit of photovoltaic (PV) modules. To estimate the other parameters of the five-parameter model, the suggested approach combines numerical computations with an iterative adjustment of the shunt resistance (R_sh). Under standard test conditions (STC), a series of equations must be solved using the parameters provided in the manufacturer's data sheet. The goal of the parameter extraction criterion is to minimize the discrepancy between the power supplied by the data sheet at the maximum power point (MPP) and the simulated power.Two different solar module types monocrystalline silicon (Mono-Si) and polycrystalline silicon (Poly-Si) representing different technologies are examined in order to verify the efficacy of the suggested approach. Simulated findings are compared with characteristics taken from manufacturers' data sheets and those obtained from methods already in use in the literature, using the Relative Error as statistical criteria to assess the validity of the proposed method, the finding results are compared with the articles “A new method to extract the equivalent circuit parameters of a photovoltaic panel”[1] and “A simple iterative method to determine the electrical parameters of photovoltaic cell”[2], in addition to manufacturer data. The proposed method presents an optimum compromise between simplicity and efficiency, outperforming conventional techniques described in the literature and offering competitive performance compared with those presented in the articles by Chaibi et al.

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Periodical:

Materials Science Forum (Volume 1173)

Pages:

71-81

DOI:

https://doi.org/10.4028/p-wBLwU4

Citation:

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Online since:

December 2025

Authors:

Salma Sandoud, Kaoutar Alami, Yaden Mohammed Faysal*, Abdelaziz Fri, Mohamed Salhi

Keywords:

Climate-Adapted Modeling, Electrical Parameter Identification, Iterative Optimization Algorithm, Monocrystalline and Polycrystalline Technologies, Photovoltaic Module Characterization, Shunt Resistance, Single-Diode Equivalent Model, STC-Based Extraction

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© 2025 Trans Tech Publications Ltd. All Rights Reserved

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* - Corresponding Author

References

[1] Y. Chaibi, M. Salhi, A. El-jouni, et A. Essadki, « A new method to extract the equivalent circuit parameters of a photovoltaic panel », Sol. Energy, vol. 163, p.376‑386, mars 2018.

DOI: 10.1016/j.solener.2018.02.017

Google Scholar

[2] Y. Chaibi, A. Allouhi, et M. Salhi, « A simple iterative method to determine the electrical parameters of photovoltaic cell », J. Clean. Prod., vol. 269, p.122363, oct. 2020.

DOI: 10.1016/j.jclepro.2020.122363

Google Scholar

[3] A. Allouhi et M. Benzakour Amine, « Effect analysis on energetic, exergetic and financial performance of a flat plate collector with heat pipes », Energy Convers. Manag., vol. 195, p.274‑289, sept. 2019.

DOI: 10.1016/j.enconman.2019.04.081

Google Scholar

[4] P. Choudhary et R. K. Srivastava, « Sustainability perspectives- a review for solar photovoltaic trends and growth opportunities », J. Clean. Prod., vol. 227, p.589‑612, août 2019.

DOI: 10.1016/j.jclepro.2019.04.107

Google Scholar

[5] Y. Chaibi, M. Malvoni, A. Allouhi, et S. Mohamed, « Data on the I–V characteristics related to the SM55 monocrystalline PV module at various solar irradiance and temperatures », Data Brief, vol. 26, p.104527, oct. 2019.

DOI: 10.1016/j.dib.2019.104527

Google Scholar

[6] A. Allouhi, «Advances on solar thermal cogeneration processes based on thermoelectric devices: A review», Sol. Energy Mater. Sol. Cells, vol. 200, p.109954, sept. 2019.

DOI: 10.1016/j.solmat.2019.109954

Google Scholar

[7] R. Abbassi, A. Abbassi, M. Jemli, et S. Chebbi, « Identification of unknown parameters of solar cell models: A comprehensive overview of available approaches », Renew. Sustain. Energy Rev., vol. 90, p.453‑474, juill. 2018.

DOI: 10.1016/j.rser.2018.03.011

Google Scholar

[8] M.-R. Chen, J.-H. Chen, G.-Q. Zeng, K.-D. Lu, et X.-F. Jiang, « An improved artificial bee colony algorithm combined with extremal optimization and Boltzmann Selection probability », Swarm Evol. Comput., vol. 49, p.158‑177, sept. 2019.

DOI: 10.1016/j.swevo.2019.06.005

Google Scholar

[9] D. Yousri, D. Allam, M. B. Eteiba, et P. N. Suganthan, « Static and dynamic photovoltaic models' parameters identification using Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants », Energy Convers. Manag., vol. 182, p.546‑563, févr. 2019.

DOI: 10.1016/j.enconman.2018.12.022

Google Scholar

[10] K. Yu, J.J. Liang, B.Y. Qu, X. Chen, et H. Wang, « Parameters identification of photovoltaic models using an improved JAYA optimization algorithm », Energy Convers. Manag., vol. 150, p.742‑753, oct. 2017.

DOI: 10.1016/j.enconman.2017.08.063

Google Scholar

[11] M.A. De Blas, J.L. Torres, E. Prieto, et A. Garcı́a, « Selecting a suitable model for characterizing photovoltaic devices », Renew. Energy, vol. 25, no 3, p.371‑380, mars 2002.

DOI: 10.1016/S0960-1481(01)00056-8

Google Scholar

[12] D. Sera, R. Teodorescu, et P. Rodriguez, « PV panel model based on datasheet values », in 2007 IEEE International Symposium on Industrial Electronics, Vigo, Spain: IEEE, juin 2007, p.2392‑2396.

DOI: 10.1109/ISIE.2007.4374981

Google Scholar

[13] M. G. Villalva, J. R. Gazoli, et E. R. Filho, « Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays », IEEE Trans. Power Electron., vol. 24, no 5, p.1198‑1208, mai 2009.

DOI: 10.1109/TPEL.2009.2013862

Google Scholar

[14] S. Lineykin, M. Averbukh, et A. Kuperman, « An improved approach to extract the single-diode equivalent circuit parameters of a photovoltaic cell/panel », Renew. Sustain. Energy Rev., vol. 30, p.282‑289, févr. 2014.

DOI: 10.1016/j.rser.2013.10.015

Google Scholar

[15] G. Walker, « EVALUATING MPPT CONVERTER TOPOLOGIES USING A MATLAB PV MODEL ».

Google Scholar

[16] M. G. Jaboori, M. M. Saied, et A. A. R. Hanafy, « A contribution to the simulation and design optimization of photovoltaic systems », IEEE Trans. Energy Convers., vol. 6, no 3, p.401‑406, sept. 1991.

DOI: 10.1109/60.84313

Google Scholar

[17] T. Ikegami, T. Maezono, F. Nakanishi, Y. Yamagata, et K. Ebihara, « Estimation of equivalent circuit parameters of PV module and its application to optimal operation of PV system », Sol. Energy Mater. Sol. Cells, vol. 67, no 1‑4, p.389‑395, mars 2001.

DOI: 10.1016/S0927-0248(00)00307-X

Google Scholar

[18] A. R. Jordehi, « Parameter estimation of solar photovoltaic (PV) cells: A review », Renew. Sustain. Energy Rev., vol. 61, p.354‑371, août 2016.

DOI: 10.1016/j.rser.2016.03.049

Google Scholar

[19] D. S. Pillai et N. Rajasekar, « Metaheuristic algorithms for PV parameter identification: A comprehensive review with an application to threshold setting for fault detection in PV systems », Renew. Sustain. Energy Rev., vol. 82, p.3503‑3525, févr. 2018.

DOI: 10.1016/j.rser.2017.10.107

Google Scholar