Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/63C2EE4D-E5D6-40EA-8CB8-1601D8E0819E | ||
| Year | 2023 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
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| Bibliographic Citation | T. Taleshian, A. Ranjbar N., M. Malekzadeh, and J. Sadati, “Parameters identification of photovoltaic solar cells using FIPSO-SQP algorithm,” Optik, vol. 283, July 2023, doi: 10.1016/j.ijleo.2023.170900. https://doi.org/10.1016/j.ijleo.2023.170900 | ||
| Appears in Collections |
In this paper a heuristic algorithm called flexible improved particle swarm optimization (FIPSO) and a gradient based algorithm called sequential quadratic programming (SQP) are combined together to be used for global optimal searching of PV parameters. Some significant features of the proposed method are: balancing between exploration and exploitation phases, providing results with higher accuracy, having higher convergence speed and performing better global search. To validate the performance of the proposed technique, it is used to identify parameters of a single diode (SD), a double diode (DD) and a triple diode (TD) PV model and an actual PV module. Results are compared with those obtained by some recent and well-established methods. Simulation results verify the superiority of the proposed FIPSO-SQP method to the other methods in terms of the convergence time (2.77 s) and accuracy (root mean square error of 9.8602e-4 for SD model, 9.8177e-4 for DD model, 9.81164e-4 for TD model and 0.016450 for PV module).
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