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Parameter Estimation of Photovoltaic Cell/Modules Using Bonobo Optimizer

Author

Listed:
  • Abdullrahman A. Al-Shamma’a

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Hammed O. Omotoso

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Fahd A. Alturki

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Hassan. M. H. Farh

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Abdulaziz Alkuhayli

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Khalil Alsharabi

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Abdullah M. Noman

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

Abstract

In this paper, a new application of Bonobo (BO) metaheuristic optimizer is presented for PV parameter extraction. Its processes depict a reproductive approach and the social conduct of Bonobos. The BO algorithm is employed to extract the parameters of both the single diode and double diode model. The good performance of the BO is experimentally investigated on three commercial PV modules (STM6-40 and STP6-120/36) and an R.T.C. France silicon solar cell under various operating circumstances. The algorithm is easy to implement with less computational time. BO is extensively compared to other state of the art algorithms, manta ray foraging optimization (MRFO), artificial bee colony (ABO), particle swarm optimization (PSO), flower pollination algorithm (FPA), and supply-demand-based optimization (SDO) algorithms. Throughout the 50 runs, the BO algorithm has the best performance in terms of minimal simulation time for the R.T.C. France silicon, STM6-40/36 and STP6-120/36 modules. The fitness results obtained through root mean square (RMSE), standard deviation (SD), and consistency of solution demonstrate the robustness of BO.

Suggested Citation

  • Abdullrahman A. Al-Shamma’a & Hammed O. Omotoso & Fahd A. Alturki & Hassan. M. H. Farh & Abdulaziz Alkuhayli & Khalil Alsharabi & Abdullah M. Noman, 2021. "Parameter Estimation of Photovoltaic Cell/Modules Using Bonobo Optimizer," Energies, MDPI, vol. 15(1), pages 1-22, December.
    • Handle: RePEc:gam:jeners:v:15:y:2021:i:1:p:140-:d:711387
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    References listed on IDEAS

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    1. Fahd A. Alturki & Abdullrahman A. Al-Shamma’a & Hassan M. H. Farh, 2020. "Simulations and dSPACE Real-Time Implementation of Photovoltaic Global Maximum Power Extraction under Partial Shading," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    2. Biswas, Partha P. & Suganthan, P.N. & Wu, Guohua & Amaratunga, Gehan A.J., 2019. "Parameter estimation of solar cells using datasheet information with the application of an adaptive differential evolution algorithm," Renewable Energy, Elsevier, vol. 132(C), pages 425-438.
    3. Humada, Ali M. & Hojabri, Mojgan & Mekhilef, Saad & Hamada, Hussein M., 2016. "Solar cell parameters extraction based on single and double-diode models: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 494-509.
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