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Parameter Estimation of Static/Dynamic Photovoltaic Models Using a Developed Version of Eagle Strategy Gradient-Based Optimizer

Author

Listed:
  • Abdelhady Ramadan

    (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)

  • Mohamed H. Hassan

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

  • Marcos Tostado-Véliz

    (Electrical Engineering Department, University of Jaen, 23071 Jaén, Spain)

  • Ali M. Eltamaly

    (K.A.CARE Energy Research and Innovation Center at Riyadh, Riyadh 11451, Saudi Arabia
    Sustainable Energy Technologies Center, King Saud University, Riyadh 11421, Saudi Arabia
    Electrical Engineering Department, Mansoura University, Mansoura 35516, Egypt)

Abstract

The global trend towards renewable energy sources, especially solar energy, has had a significant impact on the development of scientific research to manufacture high-performance solar cells. The issue of creating a model that simulates a solar module and extracting its parameter is essential in designing an improved and high performance photovoltaic system. However, the nonlinear nature of the photovoltaic cell increases the challenge in creating this model. The application of optimization algorithms to solve this issue is increased and developed rapidly. In this paper, a developed version of eagle strategy GBO with chaotic (ESCGBO) is proposed to enhance the original GBO performance and its search efficiency in solving difficult optimization problems such as this. In the literature, different PV models are presented, including static and dynamic PV models. Firstly, in order to evaluate the effectiveness of the proposed ESCGBO algorithm, it is executed on the 23 benchmark functions and the obtained results using the proposed algorithm are compared with that obtained using three well-known algorithms, including the original GBO algorithm, the equilibrium optimizer (EO) algorithm, and wild horse optimizer (WHO) algorithm. Furthermore, both of original GBO and developed ESCGBO are applied to estimate the parameters of single and double diode as static models, and integral and fractional models as examples for dynamic models. The results in all applications are evaluated and compared with different recent algorithms. The results analysis confirmed the efficiency, accuracy, and robustness of the proposed algorithm compared with the original one or the recent optimization algorithms.

Suggested Citation

  • Abdelhady Ramadan & Salah Kamel & Mohamed H. Hassan & Marcos Tostado-Véliz & Ali M. Eltamaly, 2021. "Parameter Estimation of Static/Dynamic Photovoltaic Models Using a Developed Version of Eagle Strategy Gradient-Based Optimizer," Sustainability, MDPI, vol. 13(23), pages 1-29, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13053-:d:687628
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    References listed on IDEAS

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    Cited by:

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    2. Ahmed. H. A. Elkasem & Salah Kamel & Mohamed H. Hassan & Mohamed Khamies & Emad M. Ahmed, 2022. "An Eagle Strategy Arithmetic Optimization Algorithm for Frequency Stability Enhancement Considering High Renewable Power Penetration and Time-Varying Load," Mathematics, MDPI, vol. 10(6), pages 1-38, March.
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    4. Ramakanta Jena & Ritesh Dash & Kalvakurthi Jyotheeswara Reddy & Prasanta Kumar Parida & Chittathuru Dhanamjayulu & Sarat Chandra Swain & S. M. Muyeen, 2023. "Enhancing Efficiency of Grid-Connected Solar Photovoltaic System with Particle Swarm Optimization & Long Short-Term Memory Hybrid Technique," Sustainability, MDPI, vol. 15(11), pages 1-24, May.
    5. El-Sattar, Hoda Abd & Kamel, Salah & Hassan, Mohamed H. & Jurado, Francisco, 2022. "An effective optimization strategy for design of standalone hybrid renewable energy systems," Energy, Elsevier, vol. 260(C).
    6. Xiaofei Li & Zhao Wang & Yinnan Liu & Haifeng Wang & Liusheng Pei & An Wu & Shuang Sun & Yongjun Lian & Honglu Zhu, 2023. "A Novel Operating State Evaluation Method for Photovoltaic Strings Based on TOPSIS and Its Application," Sustainability, MDPI, vol. 15(9), pages 1-16, April.
    7. Abdelhady Ramadan & Salah Kamel & I. Hamdan & Ahmed M. Agwa, 2022. "A Novel Intelligent ANFIS for the Dynamic Model of Photovoltaic Systems," Mathematics, MDPI, vol. 10(8), pages 1-14, April.

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