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A High Performance Optimizing Method for Modeling Photovoltaic Cells and Modules Array Based on Discrete Symbiosis Organism Search

Author

Listed:
  • Chaabane Bouali

    (Application Laboratory, Energy Efficiency and Renewable Energy (LAPER) UR17ES11, Department of Physics, Faculty of Sciences of Tunis, University Tunis El Manar, Tunis 1068, Tunisia)

  • Horst Schulte

    (Department of Engineering I, Control Engineering Group, University of Applied Sciences Berlin (HTW), 13353 Berlin, Germany)

  • Abdelkader Mami

    (Application Laboratory, Energy Efficiency and Renewable Energy (LAPER) UR17ES11, Department of Physics, Faculty of Sciences of Tunis, University Tunis El Manar, Tunis 1068, Tunisia)

Abstract

Exact models are a necessary prerequisite for optimal hardware configuration and the design of high-performance controllers. The photovoltaic system is considered a dynamic nonlinear multimodal system, where an optimization method must be used to resolve non-linearity and to identify the parameters describing the models of such systems. This has incited several researchers to work on and to develop several optimization methods. Recently, a number of methods have been proposed, including deterministic approaches, as well as probabilistic and stochastic numerical approaches, that aimed at obtaining a more accurate model for the PV cell and module array. This paper demonstrates the application of a performance optimization method based on discrete symbiotic organism search (DSOS), that mimics the behaviors of an organism in an ecosystem to survive. The high performance of such a method is attributable to the simplicity of the algorithm used; this algorithm is different from other heuristic algorithms, in that the GA needs two tuning parameters, i.e., the cross over and mutation rate, while the harmony search needs three rules to adjust and improvise new harmony, being memory consideration, pitch adjustment, and random choosing. Meanwhile, in the ABC algorithm, three phases are introduced to find the best food source, that is, the employed bee, the onlooker bee, and the scout bee phases, while the DSOS algorithm did not need any tuning parameters, wherein the proposed algorithm was used in both a single diode and double diode model across three test cases in the study. Compared to other previously published works, the level of performance of the algorithm is high in both implementation and accuracy; the DSOS algorithm is more capable of reaching the best set of solutions. The Mann-Whitney-Wilcoxon test to evaluate the discrete solutions of the algorithm for multiple runs with a 5% degree of confidence was evaluated and performed with a good level of accuracy.

Suggested Citation

  • Chaabane Bouali & Horst Schulte & Abdelkader Mami, 2019. "A High Performance Optimizing Method for Modeling Photovoltaic Cells and Modules Array Based on Discrete Symbiosis Organism Search," Energies, MDPI, vol. 12(12), pages 1-32, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2246-:d:239232
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    References listed on IDEAS

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    Cited by:

    1. Ewa Klugmann-Radziemska, 2020. "Shading, Dusting and Incorrect Positioning of Photovoltaic Modules as Important Factors in Performance Reduction," Energies, MDPI, vol. 13(8), pages 1-12, April.
    2. Kezhen Liu & Yumin Mao & Xueou Chen & Jiedong He & Min Dong, 2023. "Research on Dynamic Modeling and Parameter Identification of the Grid-Connected PV Power Generation System," Energies, MDPI, vol. 16(10), pages 1-17, May.
    3. Varaha Satra Bharath Kurukuru & Ahteshamul Haque & Mohammed Ali Khan & Subham Sahoo & Azra Malik & Frede Blaabjerg, 2021. "A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems," Energies, MDPI, vol. 14(15), pages 1-35, August.

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