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Parameters Identification of Solar PV Using Hybrid Chaotic Northern Goshawk and Pattern Search

Author

Listed:
  • Habib Satria

    (CoE-PUIN, Faculty of Engineering, Universitas Medan Area, Medan 20223, Indonesia)

  • Rahmad B. Y. Syah

    (CoE-PUIN, Faculty of Engineering, Universitas Medan Area, Medan 20223, Indonesia)

  • Moncef L. Nehdi

    (Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4M6, Canada)

  • Monjee K. Almustafa

    (Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4M6, Canada)

  • Abdelrahman Omer Idris Adam

    (AADC—AL-Ain Distribution Company, Abu Dhabi P.O. Box 1065, United Arab Emirates)

Abstract

This article proposes an effective evolutionary hybrid optimization method for identifying unknown parameters in photovoltaic (PV) models based on the northern goshawk optimization algorithm (NGO) and pattern search (PS). The chaotic sequence is used to improve the exploration capability of the NGO algorithm technique while evading premature convergence. The suggested hybrid algorithm, chaotic northern goshawk, and pattern search (CNGPS), takes advantage of the chaotic NGO algorithm’s effective global search capability as well as the pattern search method’s powerful local search capability. The effectiveness of the recommended CNGPS algorithm is verified through the use of mathematical test functions, and its results are contrasted with those of a conventional NGO and other effective optimization methods. The CNGPS is then used to extract the PV parameters, and the parameter identification is defined as an objective function to be minimized based on the difference between the estimated and experimental data. The usefulness of the CNGPS for extraction parameters is evaluated using three distinct PV models: SDM, DDM, and TDM. The numerical investigates illustrate that the new algorithm may produce better optimum solutions and outperform previous approaches in the literature. The simulation results display that the novel optimization method achieves the lowest root mean square error and obtains better optima than existing methods in various solar cells.

Suggested Citation

  • Habib Satria & Rahmad B. Y. Syah & Moncef L. Nehdi & Monjee K. Almustafa & Abdelrahman Omer Idris Adam, 2023. "Parameters Identification of Solar PV Using Hybrid Chaotic Northern Goshawk and Pattern Search," Sustainability, MDPI, vol. 15(6), pages 1-24, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5027-:d:1095076
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    2. Hossam Hassan Ali & Mohamed Ebeed & Ahmed Fathy & Francisco Jurado & Thanikanti Sudhakar Babu & Alaa A. Mahmoud, 2023. "A New Hybrid Multi-Population GTO-BWO Approach for Parameter Estimation of Photovoltaic Cells and Modules," Sustainability, MDPI, vol. 15(14), pages 1-33, July.

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