Abstract
In this paper, the seven traditional models of photovoltaic (PV) modules are reviewed comprehensively to find out the appropriate model for reliability. All the models are validated using the Matlab code and graphical comparisons between models are made. The accuracy and convergence of each model is evaluated using the data of manufactured PV panels. Then, a novel model is proposed showing its consistent performance. The three most key parameters of the single-diode model are self-revised to adapt to various types of PV modules. This new method is verified in three types of PV panels’ data measured by the National Renewable Energy Laboratory (NREL), USA. The validated data show promising results when the error RMSEs’ range of the proposed model is under 0.36.
Highlights
The rapid exhaustion of conventional energy resources, such as coal, crude oil, and natural gas, has been threatening to energy security in the world
Since the results have shown the promise of this method, it could be used in predicting the performance of a PV panel
These data are slightly different from the tabular data provided by manufacturers in the datasheets because they are graphically extracted from the characteristic voltage-current (I-V)curves in the datasheets
Summary
The rapid exhaustion of conventional energy resources, such as coal, crude oil, and natural gas, has been threatening to energy security in the world. Concerning a 100% renewable goal in the few decades worldwide, renewable energy has been becoming a hot topic in the research community. By 2050, solar PV could become the second-largest power generation source, behind the wind power. This growth would meet 25% of the total energy demand globally [2]. To estimate the economic feasibility of a solar PV system, the evaluation of the output power of a PV plant needs to be addressed. Some optimization techniques are employed in metaheuristic algorithms, and have been applied to obtain the model parameters as follows: Simulated annealing [4], bacterial foraging algorithm [5], genetic algorithm [6], differential evolution [7], partial algorithm [8], artificial bee colony [9], simplified swarm optimization [10], etc
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