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Marine predators algorithm for parameters estimation of photovoltaic modules considering various weather conditions.

Authors :
Sattar, Montaser Abd El
Al Sumaiti, Ameena
Ali, Hamdi
Diab, Ahmed A. Zaki
Source :
Neural Computing & Applications; Sep2021, Vol. 33 Issue 18, p11799-11819, 21p
Publication Year :
2021

Abstract

The accurate models of photovoltaic systems are the core of solar energy studies that are describing the system performance and behavior under different operating conditions. The improving of photovoltaic models based on optimizing method is recently the main simulation tool to construct I-V and P–V characteristic curves with the aid of system parameters. These parameters are extracted by using powerful optimal techniques that are building-up from datasheet of manufacturers or experimental data. This paper presents a novel proposed photovoltaic model based on Marine Predators Algorithm to estimate the optimal model parameters of solar cells or modules. Also, it can extract parameters of single diode, double diode and three diode models. Moreover, the Route Mean Square Error value between each model computed parameters and measured results of photovoltaic components are considered as the objective function. R.T.C. France Solar Cell, Photowatt-PWP201 and thin-film photovoltaic modules have been implemented to extract parameters for all three previously mentioned models at different irradiance intensities or temperature degrees. The proposed algorithm results of such cells and modules for each diode model are compared with other research works and manufacturer's results. Moreover, the evaluation of proposed algorithm has been presented considering the complexity analysis and statistical tests. The compared results show that, the accuracy of algorithm results is the best and their I-V and P–V characteristic curves are highly coinciding with manufacturer's curves. Therefore, the results of the proposed algorithm are satisfied with high superiority and better reliability to optimize parameters under different operating conditions. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09410643
Volume :
33
Issue :
18
Database :
Complementary Index
Journal :
Neural Computing & Applications
Publication Type :
Academic Journal
Accession number :
152043288
Full Text :
https://doi.org/10.1007/s00521-021-05822-0