Extraction of Solar Module Parameters using a Novel Optimization Technique

Authors

  • Hossam E. Ahmed Department of Electrical Engineering, College of Engineering, Northern Border University, Saudi Arabia | Electrical Engineering Department, Benha Faculty of Engineering, Benha University, Egypt
  • Yehya I. Mesalam Department of Industrial Engineering, College of Engineering, Northern Border University, Saudi Arabia | Industrial Engineering Department, Faculty of Engineering, Zagazig University, Egypt
  • Shaaban M. Shaaban Department of Electrical Engineering, College of Engineering, Northern Border University, Saudi Arabia | Department of Engineering Basic Science, Faculty of Engineering, Menoufia University, Egypt
Volume: 14 | Issue: 2 | Pages: 13112-13117 | April 2024 | https://doi.org/10.48084/etasr.6760

Abstract

The parameters of a Photovoltaic (PV) model are pivotal in gauging its efficiency under varying sunlight irradiances, temperatures, and different load scenarios. Determining these PV model parameters poses a complex non-linear optimization challenge. This study is based on a new metaheuristic optimization algorithm called the Pelican Optimization Algorithm (POA) to discern the unknown parameters of the PV model. The suggested POA algorithm underwent testing using a monocrystalline panel, encompassing its single-diode configuration. The objective function is designed to minimize the root of the mean squared errors between the predicted and actual current values, adhering to specific parameter constraints. Various statistical error metrics were utilized to emphasize the performance of the proposed algorithm. A comparative analysis with other well-established algorithms was conducted, indicating that POA stands out as highly competitive since it showcases superior efficiency in parameter identification compared to its counterparts.

Keywords:

PV cells, modeling, optimization, parameter extraction, POA

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How to Cite

[1]
H. E. Ahmed, Y. I. Mesalam, and S. M. Shaaban, “Extraction of Solar Module Parameters using a Novel Optimization Technique”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13112–13117, Apr. 2024.

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