Triple diode parameter estimation of solar PV cell using hybrid algorithm

• Published in: International journal of environmental science and technology (Tehran) Vol. 19; no. 5; pp. 4265 - 4288
• Main Authors: Singla, M. K., Nijhawan, P.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2022
• Subjects: algorithms; Aquatic Pollution; diodes; Earth and Environmental Science; Ecotoxicology; electricity; Environment; Environmental Chemistry; environmental science; Environmental Science and Engineering; Original Paper; Soil Science & Conservation; solar collectors; technology; temperature; Waste Water Technology; Water Management; Water Pollution Control; Ambient temperature variation; Triple diode model; Error analysis; GWOCSA
• ISSN: 1735-1472; 1735-2630
• DOI: 10.1007/s13762-021-03286-2

Solar photovoltaic (PV) systems are now one of the most prominent green energy technologies for producing a significant proportion of electricity. With the increased attention towards solar PV-based systems, the effective and precise estimation of PV cell parameters has received considerable attention from researchers. Extracting the parameters of the solar PV model is quite important for precise modelling, assessment and control of the PV system. Mostly theoretical, computational and meta-heuristic in the last few years have been proposed which extract parameters of PV cell based on the experimental results. Extracting PV model parameters, however, remains a major challenge. The application of a new hybrid algorithm that relies on the Grey Wolf Optimizer and Cuckoo Search Algorithm is being proposed in this manuscript to extract the parameters of various PV cell models. The parameter optimization results are obtained using GWOCSA and are further compared with those obtained with five other algorithms, i.e. PSO, MVO, SCA, CSA, and GWO. The complete error analysis is carried out for TDM of PV cells to establish the superiority of GWOCSA. The superiority of proposed algorithm is established using ranking test, statistical error analysis, and sensitivity temperature variation.