Szczegóły publikacji

Opis bibliograficzny

Autorzy (4)

• Sharma Pankaj
• Raju Saravanakumar
• AGHSalgotra Rohit
• Gandomi Amir H.

Słowa kluczowe

Dane bibliometryczne

ID BaDAP	150422
Data dodania do BaDAP	2023-12-05
Tekst źródłowy	URL
DOI	10.1016/j.egyr.2023.11.012
Rok publikacji	2023
Typ publikacji	artykuł w czasopiśmie
Otwarty dostęp
Creative Commons
Czasopismo/seria	Energy Reports

Abstract

Integrating solar photovoltaic (PV) systems into the modern power grid introduces a variety of new problems. The accurate modelling of PV is required to strengthen the system characteristics in simulation environments. Modelling such PV systems is reflected by a nonlinear I–V characteristic curve behaviour with numerous unknown parameters because there is insufficient data in the cells’ datasheet. As a result, it is always a priority to identify these unknown parameters. To extract features of solar modules and build high-accuracy models for modelling, control, and optimization of PV systems, current–voltage data is required. A hybrid evolutionary algorithm is proposed in this paper for precise and effective parameter estimation from experimental data of various PV models. The proposed algorithm is named as hybrid flower grey differential (HFGD) algorithm and is based on the hybridization of flower pollination algorithm (FPA), grey wolf optimizer (GWO), and differential evolution (DE) algorithm. For performance evaluation, CEC 2019 benchmark data set is used. To increase the accuracy of the output solutions, we also combined the Newton–Raphson approach with the proposed algorithm. Four PV cells/modules with diverse characteristics, including RTC France Single Diode Model (SDM), RTC France Double DM (DDM), Amorphous Silicon aSi:H, and PVM 752 GaAs Thin-Film, are used to validate the effectiveness as well as the feasibility of the proposed algorithm. The parameter results obtained through the utilization of HFGD algorithm have been compared with other evolutionary algorithms through aspects of precision, reliability, and convergence. Based on the outcomes of the comparison, it has been seen that the HFGD algorithm obtained the lowest root-mean-square error (RMSE) value. Friedman’s rank and Wilcoxon test are carried out for the statistical analysis of the proposed work. The I–V and P–V characteristics are drawn along with the box plot for different PV cells/modules. Statistical and experimental results show the superiority of the proposed algorithm with respect to its counterpart.

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