Hill Publishing Group | contact@hillpublisher.com

Hill Publishing Group

Location:Home / Journals / Journal of Electrical Power & Energy Systems /


A New Approach for Estimating Parameters in PV Cell Models based on Odd Polynomial Regression

Date: January 21,2022 |Hits: 918 Download PDF How to cite this paper

Ahmed Abdolkhalig*, Ashraf Mohamed, Fatihe Abusief

Department of Electrical Engineering, The University of Tobruk, 4004, Tobruk, Butnan, Libya.

*Corresponding author: Ahmed Abdolkhalig


This paper proposes a simple approach for estimating three of the parameter values of photovoltaic cell that can be modelled as a single-diode equivalent circuit model. The proposed method relies on the assumption that in the silicon-based single-diode equivalent circuit, the shunt resistance has a very high value and thus, its current effect can be neglected. This negligence can enable us to easily convert any photo-illuminated current based model from a logarithmic regression model to a simple odd polynomial regression model. The resulted polynomial regression model can enable us to simply estimate three parameters of the intrinsic parameters of single-diode equivalent circuit model and also, it can be applied to the characterization of any typical photovoltaic cell at varied temperatures. Root mean square error would be considered as an accuracy criterion to evaluate the estimation performance errors when the degree of the polynomial is iterated. The method is both analytically and the soft computing are covered and finally, results are discussed.


[1] Almaktar, M., et al. (2015). Artificial neural network‐based photovoltaic module temperature estimation for tropical climate of Malaysia and its impact on photovoltaic system energy yield. Progress in Photovoltaics: Research and Applications, 2015. 23(3): 302-318.

[2] De Leone, R., M. Pietrini, and A. Giovannelli. (2015). Photovoltaic energy production forecast using support vector regression. Neural Computing and Applications, 2015, 26(8): 1955-1962.

[3] Ibrahim, S., et al. (2012). Linear regression model in estimating solar radiation in Perlis. Energy Procedia, 2012, 18: 1402-1412.

[4] Rizwan, M., et al. (2014). Fuzzy logic based modeling and estimation of global solar energy using meteorological parameters. Energy, 2014, 70: 685-691.

[5] Wang, G., Y. Su, and L. Shu. (2016). One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models. Renewable Energy, 2016, 96: 469-478.

[6] Kanwal, S., et al. (2018). Gaussian process regression based inertia emulation and reserve estimation for grid interfaced photovoltaic system. Renewable Energy, 2018, 126: 865-875.

[7] Di Piazza, M. C., A. Ragusa, and G. Vitale. (2009). Identification of photovoltaic array model parameters by robust linear regression methods. In International Conference on Renewable Energies and Power Quality (ICREPQ'09). 2009.

[8] Khan, S. A., et al. (2021). Chaos Induced Coyote Algorithm (CICA) for Extracting the Parameters in a Single, Double, and Three Diode Model of a Mono-Crystalline, Polycrystalline, and a Thin-Film Solar PV Cell. Electronics, 2021, 10(17): 2094.

[9] Mdzinarishvili, T., et al. (2020). Determination of the solar rotation parameters via orthogonal polynomials. Advances in Space Research, 2020, 65(7): 1843-1851.

[10] Kabir, F., et al. (2019). Estimation of behind-the-meter solar generation by integrating physical with statistical models. In 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). 2019. IEEE.

[11] Mouatasim, A. E. and Y. Darmane. (2018). Regression analysis of a photovoltaic (PV) system in FPO. In AIP Conference Proceedings. 2018. AIP Publishing LLC.

[12] Thomopoulos, N. T. (2012). Essentials of Monte Carlo simulation: Statistical methods for building simulation models. 2012: Springer Science & Business Media.

[13] Bird, G. (1981). Monte-Carlo simulation in an engineering context. Progress in Astronautics and Aeronautics, 1981, 74: 239-255.

How to cite this paper

A New Approach for Estimating Parameters in PV Cell Models based on Odd Polynomial Regression

How to cite this paper: Ahmed Abdolkhalig, Ashraf Mohamed, Fatihe Abusief. (2022) A New Approach for Estimating Parameters in PV Cell Models based on Odd Polynomial Regression. Journal of Electrical Power & Energy Systems6(1), 17-23.

DOI: http://dx.doi.org/10.26855/jepes.2022.01.002

Volumes & Issues

Free HPG Newsletters

Add your e-mail address to receive free newsletters from Hill Publishing Group.

Contact us

Hill Publishing Group

8825 53rd Ave

Elmhurst, NY 11373, USA

E-mail: contact@hillpublisher.com

Copyright © 2019 Hill Publishing Group Inc. All Rights Reserved.