Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm

Mohamed Abdel-Basset, Reda Mohamed, Attia El-Fergany, Mohamed Abouhawwash and S. S. Askar
Additional contact information
Mohamed Abdel-Basset: Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt
Reda Mohamed: Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt
Attia El-Fergany: Electrical Power and Machines Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt
Mohamed Abouhawwash: Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt
S. S. Askar: Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt

Mathematics, 2021, vol. 9, issue 9, 1-23

Abstract: To simulate the behaviors of photovoltaic (PV) systems properly, the best values of the uncertain parameters of the PV models must be identified. Therefore, this paper proposes a novel optimization framework for estimating the parameters of the triple-diode model (TDM) of PV units with different technologies. The proposed methodology is based on the generalized normal distribution optimization (GNDO) with two novel strategies: (i) a premature convergence method (PCM), and (ii) a ranking-based updating method (RUM) to accelerate the convergence by utilizing each individual in the population as much as possible. This improved version of GNDO is called ranking-based generalized normal distribution optimization (RGNDO). RGNDO is experimentally investigated on three commercial PV modules (Kyocera KC200GT, Ultra 85-P and STP 6-120/36) and a solar unit (RTC Si solar cell France), and its extracted parameters are validated based on the measured dataset points extracted at generalized operating conditions. It can be reported here that the best scores of the objective function are equal to 0.750839 mA, 28.212810 mA, 2.417084 mA, and 13.798273 mA for RTC cell, KC200GT, Ultra 85-P, and STP 6-120/36; respectively. Additionally, the principal performance of this methodology is evaluated under various statistical tests and for convergence speed, and is compared with a number of the well-known recent state-of-the-art algorithms. RGNDO is shown to outperform the other algorithms in terms of all the statistical metrics as well as convergence speed. Finally, the performance of the RGNDO is validated in various operating conditions under varied temperatures and sun irradiance levels.

Keywords: renewable energy; PV triple-diode model; parameter extraction; optimization methods; premature convergence; ranking method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/9/995/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/9/995/ (text/html)

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:gam:jmathe:v:9:y:2021:i:9:p:995-:d:545122

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().