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Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer

Author

Listed:
  • Hassan Shaban

    (Faculty of Computers and Information, Minia University, Minia 61519, Egypt)

  • Essam H. Houssein

    (Faculty of Computers and Information, Minia University, Minia 61519, Egypt)

  • Marco Pérez-Cisneros

    (Departamento de Electrónica, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Av. Revolución 1500, Guadalajara 44430, Mexico)

  • Diego Oliva

    (Departamento de Electrónica, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Av. Revolución 1500, Guadalajara 44430, Mexico)

  • Amir Y. Hassan

    (Department of Power Electronic and Energy Conversion, Electronics Research Institute, Giza 12311, Egypt)

  • Alaa A. K. Ismaeel

    (Faculty of Computer Studies (FCS), Arab Open University (AOU), Madinat Sultan Qaboos P.O. Box 1596, Oman
    Faculty of Science, Minia University, Minia 61519, Egypt)

  • Diaa Salama AbdElminaam

    (Faculty of Computers and Artificial Intelligence, Benha University, Governorate 13511, Egypt
    Faculty of Computers Science, Misr International University, Governorate 13511, Egypt)

  • Sanchari Deb

    (VTT Technical Research Centre of Finland Ltd., 02044 Espoo, Finland)

  • Mokhtar Said

    (Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 43518, Egypt)

Abstract

Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to the random behavior of weather, the change in output current from a PV model is nonlinear. In this regard, a new optimization algorithm called Runge–Kutta optimizer (RUN) is applied for estimating the parameters of three PV models. The RUN algorithm is applied for the R.T.C France solar cell, as a case study. Moreover, the root mean square error (RMSE) between the calculated and measured current is used as the objective function for identifying solar cell parameters. The proposed RUN algorithm is superior compared with the Hunger Games Search (HGS) algorithm, the Chameleon Swarm Algorithm (CSA), the Tunicate Swarm Algorithm (TSA), Harris Hawk’s Optimization (HHO), the Sine–Cosine Algorithm (SCA) and the Grey Wolf Optimization (GWO) algorithm. Three solar cell models—single diode, double diode and triple diode solar cell models (SDSCM, DDSCM and TDSCM)—are applied to check the performance of the RUN algorithm to extract the parameters. the best RMSE from the RUN algorithm is 0.00098624, 0.00098717 and 0.000989133 for SDSCM, DDSCM and TDSCM, respectively.

Suggested Citation

  • Hassan Shaban & Essam H. Houssein & Marco Pérez-Cisneros & Diego Oliva & Amir Y. Hassan & Alaa A. K. Ismaeel & Diaa Salama AbdElminaam & Sanchari Deb & Mokhtar Said, 2021. "Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer," Mathematics, MDPI, vol. 9(18), pages 1-22, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2313-:d:638875
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    References listed on IDEAS

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    Cited by:

    1. Papul Changmai & Sunil Deka & Shashank Kumar & Thanikanti Sudhakar Babu & Belqasem Aljafari & Benedetto Nastasi, 2022. "A Critical Review on the Estimation Techniques of the Solar PV Cell’s Unknown Parameters," Energies, MDPI, vol. 15(19), pages 1-20, September.
    2. Nawal Rai & Amel Abbadi & Fethia Hamidia & Nadia Douifi & Bdereddin Abdul Samad & Khalid Yahya, 2023. "Biogeography-Based Teaching Learning-Based Optimization Algorithm for Identifying One-Diode, Two-Diode and Three-Diode Models of Photovoltaic Cell and Module," Mathematics, MDPI, vol. 11(8), pages 1-30, April.
    3. Khan, Taimoor Ali & Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Mehmood, Khizer & Hsu, Chung-Chian & Raja, Muhammad Asif Zahoor, 2024. "Design of Runge-Kutta optimization for fractional input nonlinear autoregressive exogenous system identification with key-term separation," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    4. Zaiyu Gu & Guojiang Xiong & Xiaofan Fu, 2023. "Parameter Extraction of Solar Photovoltaic Cell and Module Models with Metaheuristic Algorithms: A Review," Sustainability, MDPI, vol. 15(4), pages 1-45, February.
    5. Wisam Kareem Meteab & Salwan Ali Habeeb Alsultani & Francisco Jurado, 2023. "Energy Management of Microgrids with a Smart Charging Strategy for Electric Vehicles Using an Improved RUN Optimizer," Energies, MDPI, vol. 16(16), pages 1-18, August.
    6. Alma Y. Alanis, 2022. "Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications," Mathematics, MDPI, vol. 10(13), pages 1-2, July.

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