article · International Transactions on Electrical Energy Systems
A new optimization framework extracts unknown parameters for triple-diode solar cell and photovoltaic module models. The approach employs an Artificial Ecosystem-based Optimizer to identify nine unknown parameters in the equivalent circuit model. By minimising the root mean squared error between measured experimental data and estimated data, the method accurately fits operational characteristics to build a generic photovoltaic model. The framework was evaluated across three distinct commercial photovoltaic cells and modules, and benchmarked against multiple established optimization algorithms found in the literature. Numerical findings confirm that the Artificial Ecosystem-based Optimizer achieves high precision alongside a fast response when identifying parameters across multiple photovoltaic models.
Accurate mathematical modelling of solar cells and modules is critical for understanding their behaviour under operating conditions. By quickly and precisely extracting parameters from experimental data, this method supports the creation of generic, reliable photovoltaic models, helping engineers and researchers assess performance and improve solar power simulations.
The algorithm enables accurate parameter estimation for commercial photovoltaic cells and modules, which is valuable for solar energy system designers, model developers, and manufacturers seeking to calibrate device simulations. Validated using experimental data from commercial units in a comparative study, the method represents applied and tested research at an algorithmic level, though integration into commercial design or simulation software remains to be demonstrated.
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This manuscript proposes a modern optimization framework for parameter extraction of a triple-diode model of the unknown solar cell and Photovoltaic (PV) module parameters. The suggested optimization framework is based on applying a new metaheuristic optimization algorithm called Artificial Ecosystem-based Optimizer (AEO) to determine the nine unknown parameters of the triple-diode model of PV equivalent circuit model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. In this context, the root means squared error (RMSE) between the measured and estimated data is considered as the primary objective function. This objective function achieves the closeness degree between the estimated and experimental data. On the way to accomplish this study, the proposed AEO is carried out on three different commercial PV cells/modules. To assess the proposed algorithm, a comprehensive comparison study is used compared with several well-matured optimization algorithms reported in the literature. The attained numerical results prove the high precision and fast response of the proposed AEO algorithm for identifying multiple PV models.
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DOI: 10.1002/2050-7038.13043
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