Static models for implementing photovoltaic panels characteristics under various environmental conditions using improved gradient-based optimizer. (August 2022)

• Record Type: Journal Article
• Authors: Abd Elaziz, Mohamed; Almodfer, Rolla; Ahmadianfar, Iman; Ibrahim, Ibrahim Anwar; Mudhsh, Mohammed; Abualigah, Laith; Lu, Songfeng; Abd El-Latif, Ahmed A.; Yousri, Dalia
• Is Part Of: Sustainable energy technologies and assessments. Volume 52:Part B(2022)
• Publication Date: 2022-08
• Subjects: Solar energy technology -- Gradient-based optimizer -- Parameters estimation -- Single diode model -- Two diode model; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2022.102150
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: An accurate definition of the photovoltaic (PV) models is an essential task to emulate and understand the physical behavior of the PV cell/panels. The highly used PV models are the static equivalent circuits, including single and double diode models. However, the accurate definition of the static models is mainly based on their estimated parameters. Proposing a reliable Optimization-based approached is a challenging aim. So, this paper proposes a novel and efficient optimizer to identify PV single and double diode models' parameters for several PV modules using different sets of experimentally measured data. The developed method depends on improving the gradient-based optimization algorithm (GBO) using a new crossover operator to enhances agents' diversity. Furthermore, a modified local escaping operator is applied to improve exploitation of GBO. The performance of the improvement GBO (IGBO) is evaluated using different experimental datasets for numerous PV modules under several operating conditions of temperature and radiation. The efficiency of IGBO is validated through a massive comparison with a set of recent state-of-the-art techniques. Reported results, fitting curves, and convergence curves provide proof for the efficiency of IGBO in providing high qualifies results with remarkable convergence speed.