An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models

Mohamed Abdel-Basset, Reda Mohamed, Ripon K. Chakrabortty, Michael J. Ryan and Attia El-Fergany
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Mohamed Abdel-Basset: Department of Computer Science, Zagazig University, Shaibet an Nakareyah, Zagazig 44519, Egypt
Reda Mohamed: Department of Computer Science, Zagazig University, Shaibet an Nakareyah, Zagazig 44519, Egypt
Ripon K. Chakrabortty: Capability Systems Centre, School of Engineering and IT, UNSW Canberra 2052, Australia
Michael J. Ryan: Capability Systems Centre, School of Engineering and IT, UNSW Canberra 2052, Australia
Attia El-Fergany: Department of Electric Power & Machines, Zagazig University, Shaibet an Nakareyah, Zagazig 44519, Egypt

Energies, 2021, vol. 14, issue 7, 1-33

Abstract: The optimization of photovoltaic (PV) systems relies on the development of an accurate model of the parameter values for the solar/PV generating units. This work proposes a modified artificial jellyfish search optimizer (MJSO) with a novel premature convergence strategy (PCS) to define effectively the unknown parameters of PV systems. The PCS works on preserving the diversity among the members of the population while accelerating the convergence toward the best solution based on two motions: (i) moving the current solution between two particles selected randomly from the population, and (ii) searching for better solutions between the best-so-far one and a random one from the population. To confirm its efficacy, the proposed method is validated on three different PV technologies and is being compared with some of the latest competitive computational frameworks. The numerical simulations and results confirm the dominance of the proposed algorithm in terms of the accuracy of the final results and convergence rate. In addition, to assess the performance of the proposed approach under different operation conditions for the solar cells, two additional PV modules (multi-crystalline and thin-film) are investigated, and the demonstrated scenarios highlight the utility of the proposed MJSO-based methodology.

Keywords: artificial jellyfish search optimizer; premature convergence strategy; solar systems; performance measures; PV modules (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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