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Development of an Improved Bonobo Optimizer and Its Application for Solar Cell Parameter Estimation

Author

Listed:
  • Reem Y. Abdelghany

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Salah Kamel

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Hamdy M. Sultan

    (Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia 61111, Egypt)

  • Ahmed Khorasy

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Salah K. Elsayed

    (Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Mahrous Ahmed

    (Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

Abstract

Recently, photovoltaic (PV) energy has been considered one of the most exciting new technologies in the energy sector. PV power plants receive considerable attention because of their wide applications. Consequently, it is important to study the parameters of the solar cell model to control and determine the characteristics of the PV systems. In this study, an improved bonobo optimizer (IBO) was proposed to improve the performance of the conventional bonobo optimizer (BO). Both the IBO and the BO were utilized to obtain the accurate values of the unknown parameters of different mathematical models of solar cells. The proposed IBO improved the performance of the conventional BO by enhancing the exploitation (local search) and exploration (global search) phases to find the best optimal solution, where the search space was reduced using Levy flights and the sine–cosine function. Levy flights enhance the explorative phase, whereas the sine–cosine function improves the exploitation phase. Both the proposed IBO and the conventional BO were applied on single, double, and triple diode models of solar cells. To check the effectiveness of the proposed algorithm, statistical analysis based on the results of 20 runs of the optimization program was performed. The results obtained by the proposed IBO were compared with other algorithms, and all results of the proposed algorithm showed their durability and exceeded other algorithms.

Suggested Citation

  • Reem Y. Abdelghany & Salah Kamel & Hamdy M. Sultan & Ahmed Khorasy & Salah K. Elsayed & Mahrous Ahmed, 2021. "Development of an Improved Bonobo Optimizer and Its Application for Solar Cell Parameter Estimation," Sustainability, MDPI, vol. 13(7), pages 1-22, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:7:p:3863-:d:527617
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    References listed on IDEAS

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    Cited by:

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    3. Song, Dongran & Li, Ziqun & Wang, Lei & Jin, Fangjun & Huang, Chaoneng & Xia, E. & Rizk-Allah, Rizk M. & Yang, Jian & Su, Mei & Joo, Young Hoon, 2022. "Energy capture efficiency enhancement of wind turbines via stochastic model predictive yaw control based on intelligent scenarios generation," Applied Energy, Elsevier, vol. 312(C).
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    5. Eid, Heba F. & Cuevas, Erik & Mansour, Romany F., 2024. "Autonomous bonobo optimization algorithm for power allocation in wireless networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 217(C), pages 294-310.

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