Optimal Extraction of Photovoltaic Cell Parameters for the Maximization of Photovoltaic Power Output Using a Hybrid Particle Swarm Grey Wolf Optimization Algorithm
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Date
2021-08-05
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AJRSP
Abstract
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 costeffective
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.
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This article is published by AJRSP 2021 and is also available at 2706-6495
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Academic Journal of Research and Scientific Publishing | Vol 3 | Issue 28