Szczegóły publikacji

Opis bibliograficzny

Parametric estimation of photovoltaic systems using a new multi-hybrid evolutionary algorithm / Pankaj Sharma, Saravanakumar Raju, Rohit SALGOTRA, Amir H. Gandomi // Energy Reports [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2352-4847. — 2023 — vol. 10, s. 4447–4464. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 4463–4464, Abstr. — Publikacja dostępna online od: 2023-11-16. — R. Salgotra - dod. afiliacja: Middle East University, Amman, Jordan

Autorzy (4)

Słowa kluczowe

metaheuristic optimization techniquessolar PVHFGD algorithmCEC 2019 benchmarkparameter estimation

Dane bibliometryczne

ID BaDAP150422
Data dodania do BaDAP2023-12-05
Tekst źródłowyURL
Rok publikacji2023
Typ publikacjiartykuł w czasopiśmie
Otwarty dostęptak
Creative Commons
Czasopismo/seriaEnergy Reports


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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