Open Access

ARTICLE

Photovoltaic Models Parameters Estimation Based on Weighted Mean of Vectors

Mohamed Elnagi¹, Salah Kamel², Abdelhady Ramadan², Mohamed F. Elnaggar^3,4,*

1 Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Qena, 83518, Egypt
2 Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt
3 Department of Electrical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
4 Department of Electrical Power and Machines Engineering, Faculty of Engineering, Helwan University, Helwan, 11795, Egypt

* Corresponding Author: Mohamed F. Elnaggar. Email:

Computers, Materials & Continua 2023, 74(3), 5229-5250. https://doi.org/10.32604/cmc.2023.032469

Received 19 May 2022; Accepted 15 September 2022; Issue published 28 December 2022

Abstract

Renewable energy sources are gaining popularity, particularly photovoltaic energy as a clean energy source. This is evident in the advancement of scientific research aimed at improving solar cell performance. Due to the non-linear nature of the photovoltaic cell, modeling solar cells and extracting their parameters is one of the most important challenges in this discipline. As a result, the use of optimization algorithms to solve this problem is expanding and evolving at a rapid rate. In this paper, a weIghted meaN oF vectOrs algorithm (INFO) that calculates the weighted mean for a set of vectors in the search space has been applied to estimate the parameters of solar cells in an efficient and precise way. In each generation, the INFO utilizes three operations to update the vectors’ locations: updating rules, vector merging, and local search. The INFO is applied to estimate the parameters of static models such as single and double diodes, as well as dynamic models such as integral and fractional models. The outcomes of all applications are examined and compared to several recent algorithms. As well as the results are evaluated through statistical analysis. The results analyzed supported the proposed algorithm’s efficiency, accuracy, and durability when compared to recent optimization algorithms.

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Keywords

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