International peer-reviewed journal

ISSN: 2706-6495
Email: editor@ajrsp.com

Coming Issue: (64)
Submission Deadline:
26 July 2024
Date of Issue:
5 August 2024

Peer-Review statement

AJRSP follows academic conditions and rules for the arbitration and dissemination of scientific research. All published articles have undergone a rigorous peer-review process based on initial screening and final decision.

Helpful Links
  • Publication Fields
  • Publication Fees
  • AJRSP Template
  • Publication Ethics
  • Journal's Policies
  • Contact us

  • Archive
  • Current Issue
  • Archive
  • All copyrights reserved to the Academic Journal of Research and Scientific Publishing by Creative Commons License


    Optimal Extraction of Photovoltaic Cell Parameters for the Maximization of Photovoltaic Power Output Using a Hybrid Particle Swarm Grey Wolf Optimization Algorithm

    Author: Ali Abubakar(1*), Dr. Reindorf Nartey Borkor(2)
    Department of Mathematics, Kwame Nkrumah University of Science and Technology, Ghana (1,2)

    Email: a.abubakar7751@gmail.com *

    Doi: doi.org/10.52132/Ajrsp.e.2021.281


    Avoiding over-dependency on the oil-fired energy supply systems motivates many countries to integrate renewable energy into the existing energy supply systems. Solar Photovoltaic technology forms the most promising option for developing such a cost-effective and sustainable energy supply system. Generally, the current-voltage curve is used in the performance assessment and analysis of the Photovoltaic module. The accuracy of the equations for the curve depends on accurate cell parameters. However, the extraction of these parameters remains a complex stochastic nonlinear optimization problem. Many studies have been carried out to deal with such problem but still more researches need to be carried out to achieve a minimum error and a high accuracy. The existing researches ignored the variation in the meteorological data though it has a significant impact on the problem design. In this study, the Sample Average Approximation was employed to deal with the uncertainty and the hybrid optimization method was used to get the optimal parameters. The results showed that the Hybrid PSO-GWO produced the most optimal solution: Series resistance (1.4623), Shunt resistance(215.0000), Ideal diode factors (n1 = 0.9500, n2 = 1.6500) with a maximum PV power of 59.850W. The methodology produced realistic results since the variability is dealt with and the Hybrid PSO-GWO finds the optimal solution at a higher convergence rate.


    Diode, Irradiance, Monte-Carlo, Parameters, Photovoltaic, Solar, Stochastic

    Download PDF