A hybrid wind driven-based fruit fly optimization algorithm for identifying the parameters of a double-diode photovoltaic cell model considering degradation effects

• Published in: Sustainable energy technologies and assessments Vol. 50; p. 101685
• Main Authors: Ibrahim, Ibrahim Anwar, Hossain, M.J., Duck, Benjamin C.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2022
• Subjects: Current-voltage characteristic curve; Double-diode photovoltaic model; Parameter identification; Photovoltaic; Wind driven-based fruit fly optimization algorithm; Double-diode photovoltaic model; Photovoltaic; Parameter identification; Current-voltage characteristic curve; Wind driven-based fruit fly optimization algorithm
• ISSN: 2213-1388
• DOI: 10.1016/j.seta.2021.101685

•An WDFO algorithm is proposed for the seven parameters extraction model.•The WDFO model outperforms the state-of-the-art the deterministic-based models.•The performance of WDFO model is superior to the metaheuristic-based models.•WDFO algorithm converges to the optimal solution within an average of 9.15 s.•Assessment of the degradation effects on the extracted parameters is investigated. The identification of unknown parameters of photovoltaic modules is the keystone to model their performance accurately. This paper introduces a novel hybrid wind driven-based fruit fly optimization algorithm to determine a double-diode photovoltaic cell model’s seven unknown parameters. Due to the limitations of reaching a matured convergence of the classical wind driven optimization for complex multi-modal optimization problems, this paper presents a hybrid algorithm by integrating the wind driven optimization algorithm’s exploitation and fruit fly optimization algorithm’s exploration capacities. The effectiveness of the proposed model is validated using real data from three photovoltaic technologies: mono-crystalline, poly-crystalline, and thin-film. Besides, its computational efficiency and precision are compared with those of various models: deterministic- and metaheuristic-based models. The average values of the standard deviation, normalized-root-mean-square error, mean absolute percentage error, coefficient of determination, and convergence speed of the proposed model were 8.1101 × 10-9, 0.0911%, 2.5661%, 99.0115%, and 10.0112 s. for mono-crystalline PV module, 7.1129 × 10-9, 0.1029%, 2.6334%, 98.9331%, and 8.1201 s. for poly-crystalline PV module, and 6.2212 × 10-9, 0.0871%, 2.3129%, 99.1256% and 9.3211 s. for thin-film PV module. Findings indicate that the proposed model outperforms the aforementioned models in accuracy, convergence speed and feasibility. In addition, it can work blindly with any current-voltage characteristic curve on a 15-min. basis under any weather condition without the need for any initial guess or previous information about any parameter.