Identification of PV solar cells and modules parameters using the genetic algorithms: Application to maximum power extraction

• Published in: Solar energy Vol. 84; no. 5; pp. 860 - 866
• Main Authors: Zagrouba, M., Sellami, A., Bouaïcha, M., Ksouri, M.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.05.2010; Elsevier; Pergamon Press Inc
• Subjects: ALGORITHMS; Applied sciences; Diodes; ELECTRIC CONDUCTIVITY; ELECTRIC POTENTIAL; ELECTRIC POWER; Energy; Equipments, installations and applications; Exact sciences and technology; EXTRACTION; Genetic algorithms; Illuminated I– V characterization; Mathematical models; Maximum power; Maximum power point; Modules; Natural energy; OPTIMIZATION; Photovoltaic cells; Photovoltaic conversion; PHOTOVOLTAIC EFFECT; PV modules; SILICON SOLAR CELLS; Solar cells; Solar cells. Photoelectrochemical cells; SOLAR ENERGY; Volt-ampere characteristics; Silicon solar cells; PV modules; Illuminated I– V characterization; Maximum power point; Genetic algorithms; Solar cell; Genetic algorithm; Photovoltaic array; Photovoltaic system; Illuminated I―V characterization
• ISSN: 0038-092X; 1471-1257
• DOI: 10.1016/j.solener.2010.02.012

In this paper, we propose to perform a numerical technique based on genetic algorithms (GAs) to identify the electrical parameters ( I s, I ph, R s, R sh, and n) of photovoltaic (PV) solar cells and modules. These parameters were used to determine the corresponding maximum power point (MPP) from the illuminated current–voltage ( I– V) characteristic. The one diode type approach is used to model the AM1.5 I– V characteristic of the solar cell. To extract electrical parameters, the approach is formulated as a non convex optimization problem. The GAs approach was used as a numerical technique in order to overcome problems involved in the local minima in the case of non convex optimization criteria. Compared to other methods, we find that the GAs is a very efficient technique to estimate the electrical parameters of PV solar cells and modules. Indeed, the race of the algorithm stopped after five generations in the case of PV solar cells and seven generations in the case of PV modules. The identified parameters are then used to extract the maximum power working points for both cell and module.