Modelling and Parameters Extraction of Flexible Amorphous Silicon Solar Cell a-Si:H

The precise of solar cell model parameters being the prerequisite for realizing accurate photovoltaic models. Hence, the parameters identification techniques have attracted immense interest over the years among the researchers. This paper proposes a modelling and prediction of electrical intrinsic p...

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Bibliographic Details
Published in:Applied solar energy 2020, Vol.56 (1), p.1-12
Main Authors: Mohamed Louzazni, Khouya, Ahmed, Crăciunescu, Aurelian, Amechnoue, Khalid, Mussetta, Marco
Format: Article
Language:English
Subjects:
Algorithms
Amorphous silicon
Complex systems
Electric potential
Electrical Machines and Networks
Engineering
Heuristic methods
Identification methods
Mathematical models
Modelling
Nonlinear systems
Objective function
Parameter estimation
Parameter identification
Photovoltaic cells
Photovoltaics
Power Electronics
Quasi Newton methods
Silicon
Solar cells
Solar energy
Solar Engineering Materials Science
Statistical analysis
Voltage
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Summary:The precise of solar cell model parameters being the prerequisite for realizing accurate photovoltaic models. Hence, the parameters identification techniques have attracted immense interest over the years among the researchers. This paper proposes a modelling and prediction of electrical intrinsic parameter extraction method of flexible hydrogenated amorphous silicon a-Si:H solar cell, based on the meta-heuristic firefly algorithm (FA). The characteristics of solar cells are non-linear, multivariable and multi-modal and difficult to identifies the electrical intrinsic parameters by conventional and analytical methods with high accuracy. Recently, the firefly algorithm has attracted the attention to optimize the non-linear and complex systems, based on the flashing patterns and behaviour of firefly’s swarm. Besides, the proposed constrained objective function is derived from the current–voltage curve. It is the absolute errors between the experimental and calculated current and voltage values. Furthermore, the obtained results of the proposed algorithm are compared with the results obtained by quasi-Newton method (Q-N) and self-organizing migrating algorithm (SOMA). Indeed, to validate the performance of the algorithm, the statistical analyses are carried out to measure the accuracy of the estimated parameters. In the end, the theoretical results of the firefly algorithm show an excellent agreement with experimental data and more accurate compared to other compared techniques.
ISSN:0003-701X
1934-9424
DOI:10.3103/S0003701X20010090