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
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