Adaptive Particle Swarm Optimization based improved modeling of Solar Photovoltaic module for parameter determination

• Published in: e-Prime Vol. 8; p. 100621
• Main Authors: K V, Shihabudheen, G, Dileep, Sulthan, Sheik Mohammed
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2024; Elsevier
• Subjects: Adaptive particle swarm optimization (APSO); Five parameter model; parameter estimation; Spv module; ABC; MPPT; STC; SDOA; Impp; H; NRM; I; RMSE; PSO; Spv module; Adaptive particle swarm optimization (APSO); T; APSO; V; SPV; parameter estimation; GA; EA; Five parameter model; MSE; Vmpp
• ISSN: 2772-6711; 2772-6711
• DOI: 10.1016/j.prime.2024.100621

•A method of extracting five parameter model of a PV module from manusfacutres data is proposed.•Uses adaptive particle swarm optimization (APSO) with barrier constraints.•considered the effect of solar radiation and cell temperature.•Multi-crystalline and mono-crystalline PV modules technologies are investigated.•Comprehensive comparison between APSO methods, genetic algorithm (GA), differential evalution (DE) and salp swarm optimization (SSO) are conducted. Solar Photovoltaic (SPV) panel manufacturers provide a datasheet, which contains key electrical specifications of the SPV module for the user's reference. However, these data alone prove inadequate in anticipating the performance of PV systems under fluctuating atmospheric conditions. The approach put forth in this paper ascertains five parameters of a PV module using information supplied by the manufacturer. This approach considers the impact of solar radiation and cell temperature. The paper outlines the process for determining the parameters of the five-parameter model and establishes a comparison between projected current-voltage curves and manufacturer-provided data. To extract the unknown parameters of a single diode model Adaptive Particle Swarm Optimization (APSO) techniques with barrier constraints was developed. The study investigates both multi-crystalline and mono-crystalline PV module technologies to validate the efficacy of the proposed method. Results demonstrate that the proposed technique, besides being straightforward, proves adept at accurately simulating the dependable performances of PV modules. Comparative analysis reveals that the proposed methods extract parameters with minimal modeling errors and high precision, regardless of temperature fluctuations, outperforming conventional PSO methods.