Extraction of electrical parameters for one diode photovoltaic model using quasi-oppositional Rao-1 optimization algorithm

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Badis Lekouaghet
Sid-Ahmed Touil
Chabane Boubakir


Usually the model parameters of photovoltaic (PV) cells are unavailable in their datasheet provided by the manufacturers. Hence, the problem of extracting appropriate PV parameters is of high importance, and has been highly attracted by researchers. This paper presents a new method to estimate the parameters of the one diode model (ODM), namely quasi-oppositional Rao-1 optimization algorithm (QORao-1). The present method uses only addition and multiplication operations with a quasi-oppositional-based learning process, in order to improve the exploration capability of the original Rao-1 algorithm. Hence, an attractive amelioration in the Root-Mean-Square-Error (RMSE) values is acquired. Comparative performance analysis demonstrates that the QORao-1 algorithm has better performance in terms of robustness and accuracy while estimating the PV parameters than many well-known algorithms.

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How to Cite
B. . Lekouaghet, S.-A. Touil, and C. . Boubakir, “Extraction of electrical parameters for one diode photovoltaic model using quasi-oppositional Rao-1 optimization algorithm”, J. Ren. Energies, vol. 1, no. 1, pp. 143 -, Jun. 2022.


Venkateswari, R., Rajasekar, N. ‘Review on parameter estimation techniques of solar photovoltaic systems.’ International Transactions on Electrical Energy Systems, 2021; 31: e13113.

Orioli A., “An accurate one-diode model suited to represent the current voltage characteristics of crystalline and thin-film photovoltaic modules”, Renewable Energy, Jan. 2020; 145:725-743.

Lekouaghet B, Boubakir C, Boukabou A. ‘A New method to represent the IV and PV characteristics of different photovoltaic modules.’ In 2020 6th International Symposium on New and Renewable Energy (SIENR) 2021 Oct 13 (pp. 1-6). IEEE.

Batzelis E. ‘Non-iterative methods for the extraction of the single-diode model parameters of photovoltaic modules: a review and comparative assessment’. Energies. Jan 2019; 12:358.

Li S, Gong W, Gu Q. A comprehensive survey on meta-heuristic algorithms for parameter extraction of photovoltaic models. Renewable and Sustainable Energy Reviews. May 2021; 141:110828.

Farah A, Belazi A, Benabdallah F, Almalaq A, Chtourou M, Abido MA. Parameter extraction of photovoltaic models using a comprehensive learning Rao-1 algorithm. Energy Conversion and Management. 2022 Jan 15; 252:115057.

Lekouaghet B, Boukabou A, Boubakir C.: 'Estimation of the photovoltaic cells/modules parameters using an improved Rao-based chaotic optimization technique'. Energy Conversion and Management, 2021 Feb 1; 229:113722.

Jian X, Zhu Y. Parameters identification of photovoltaic models using modified Rao-1 optimization algorithm. Optik. 2021 Apr 1; 231:166439.

Deotti LM, Pereira JL, da Silva Júnior IC. ‘Parameter extraction of photovoltaic models using an enhanced Lévy flight bat algorithm’. Energy Conversion and Management. 2020 Oct 1; 221:113114.

Jiao S, Chong G, Huang C, Hu H, Wang M, Heidari AA, Chen H, Zhao X. Orthogonally adapted Harris hawks optimization for parameter estimation of photovoltaic models. Energy. 2020 Jul 15; 203:117804.

Liang J, Qiao K, Yuan M, Yu K, Qu B, Ge S, Li Y, Chen G. Evolutionary multi-task optimization for parameters extraction of photovoltaic models. Energy Conversion and Management. 2020 Mar 1; 207:112509.

Kumar C, Raj TD, Premkumar M, Raj TD. A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters. Optik. 2020 Dec 1; 223:165277.

Long W, Wu T, Xu M, Tang M, Cai S: ‘Parameters identification of photovoltaic models by using an enhanced adaptive butterfly optimization algorithm’. Energy, 2021 Aug; 229: 120750.

Abdel-Basset M, El-Shahat D, Chakrabortty R K, Ryan M: ‘Parameter estimation of photovoltaic models using an improved marine predators algorithm’. Energy Conversion and Management, 2021 Jan; 227: 113491.

Zhou W, Wang P, Heidari A. A, Zhao X, Turabieh H, Chen H. ‘Random learning gradient based optimization for efficient design of photovoltaic models’. Energy Conversion and Management, 2021, 230: 113751.

Shaban H, Houssein E. H, Cisneros M. P, Oliva D, Hassan A. Y, Ismaeel A. K, AbdElminaam D. S, Deb S, Said M.: ‘Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimize’ Mathematics, 2021 Sep; 9: 2313.

Joyce T, Herrmann JM. ‘A review of no free lunch theorems, and their implications for metaheuristic optimisation.’ Nature-inspired algorithms and applied optimization; 2018:27-51.

Rao RV, Pawar RB. Quasi-oppositional-based Rao algorithms for multi-objective design optimization of selected heat sinks. Journal of Computational Design and Engineering. 2020 Dec; 7(6):830-63.