Parameter Estimation of Photovoltaic Cell using Transit Search Optimizer

Authors

  • Hady El Said Abdel Maksoud Department of Electrical Engineering, College of Engineering, Northern Border University, Saudi Arabia | Electrical Engineering Department, Faculty of Engineering, Menoufia 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: 3 | Pages: 13967-13973 | June 2024 | https://doi.org/10.48084/etasr.6956

Abstract

In the evaluation of a Photovoltaic (PV) system's performance, precise calculation of the system's parameters is essential, as these parameters significantly influence its efficiency across various sunlight intensities, temperature ranges, and distinct load conditions. Addressing the intricate non-linear optimization problem of pinpointing these PV system parameters, the current research adopts a novel metaheuristic optimization approach, called Transit Search (TS). The proposed technique was rigorously tested on a monocrystalline solar panel, which included both single and double-diode model structures. The design of the objective function within this framework aims to diminish the square root of the average squared discrepancies between theoretical and measured current outputs, while remaining within the established parameter bounds. The proficiency of the TS algorithm was highlighted by employing a variety of statistical error indicators, underlining the latter’s effectiveness. When pitted against other established optimization algorithms through comparative analysis, TS demonstrated outstanding capabilities, evidently outperforming its contemporaries in the accurate determination of PV system parameters.

Keywords:

parameter extraction, PV cells, modeling, TS algorithm, optimization

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How to Cite

[1]
H. E. S. Abdel Maksoud and S. M. Shaaban, “Parameter Estimation of Photovoltaic Cell using Transit Search Optimizer”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 13967–13973, Jun. 2024.

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