Photovoltaic model parameters identification using Northern Goshawk Optimization algorithm

Mahmoud A. El-Dabah, Ragab A. El-Sehiemy, Hany M. Hasanien and Bahaa Saad

Energy, 2023, vol. 262, issue PB

Abstract: The massive integration of photovoltaic (PV) systems into electric power grids creates a slew of new issues in today's power systems. Accurate modeling of photovoltaic modules is critical in strengthening the characteristics of its systems in simulation assessments. Modeling such PV systems is represented by a nonlinear current-voltage characteristic curve behavior with numerous unknown parameters due to insufficient data in the cells' datasheet. This manuscript presents an application of a recently introduced optimization algorithm called Northern Goshawk Optimization (NGO) for parameter identification of the triple diode model of the PV module. Three commercial PV modules are utilized in this study to accomplish this task. These models are multi-crystalline structures like Photowatt-PWP201 and Kyocera KC200GT and mono-crystalline like Canadian Solar CS6K-280 M. The simulation results show the ability of the NGO to extract the model parameters accurately. Experimental validation of the estimated parameters using the NGO optimizer is accomplished and compared with the simulation results under various environmental conditions. The simulation results show the superiority of the NGO over competitive optimization algorithms in terms of convergence speed and accuracy. The NGO can reduce the cost function to 1.35E-05, 9.42E-05, and 0.000195 for the PWP-201, KC200GT, and Canadian Solar CS6K-M modules. Moreover, the robustness of the NGO is evaluated by the statistical analysis and the Wilcoxon rank test.

Keywords: Energy device modeling; Optimization methods; Photovoltaic systems; Renewable energy systems (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222024045
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:262:y:2023:i:pb:s0360544222024045

DOI: 10.1016/j.energy.2022.125522

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().