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Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization

Author

Listed:
  • Arooj Tariq Kiani

    (Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila 47080, Pakistan)

  • Muhammad Faisal Nadeem

    (Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila 47080, Pakistan)

  • Ali Ahmed

    (Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila 47080, Pakistan)

  • Irfan Khan

    (Marine Engineering Technology Department in a joint appointment with the Electrical and Computer Engineering Department, Texas A&M University, Galveston, TX 77553, USA)

  • Rajvikram Madurai Elavarasan

    (Electrical and Automotive parts Manufacturing unit, AA Industries, Chennai 600 123, Tamilnadu, India)

  • Narottam Das

    (School of Engineering and Technology, Central Queensland University, Melbourne, VIC 3000, Australia
    Centre for Intelligent Systems, School of Engineering and Technology, Central Queensland University, Brisbane, QLD 4000, Australia)

Abstract

Parameters associated with electrical equivalent models of the photovoltaic (PV) system play a significant role in the performance enhancement of the PV system. However, the accurate estimation of these parameters signifies a challenging task due to the higher computational complexities and non-linear characteristics of the PV modules/panels. Hence, an effective, dynamic, and efficient optimization technique is required to estimate the parameters associated with PV models. This paper proposes a double exponential function-based dynamic inertia weight (DEDIW) strategy for the optimal parameter estimation of the PV cell and module that maintains an appropriate balance between the exploitation and exploration phases to mitigate the premature convergence problem of conventional particle swarm optimization (PSO). The proposed approach (DEDIWPSO) is validated for three test systems; (1) RTC France solar cell, (2) Photo-watt (PWP 201) PV module, and (3) a practical test system (JKM330P-72, 310 W polycrystalline PV module) which involve data collected under real environmental conditions for both single- and double-diode models. Results illustrate that the parameters obtained from proposed technique are better than those from the conventional PSO and various other techniques presented in the literature. Additionally, a comparison of the statistical results reveals that the proposed methodology is highly accurate, reliable, and efficient.

Suggested Citation

  • Arooj Tariq Kiani & Muhammad Faisal Nadeem & Ali Ahmed & Irfan Khan & Rajvikram Madurai Elavarasan & Narottam Das, 2020. "Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization," Energies, MDPI, vol. 13(15), pages 1-26, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:4037-:d:394468
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    References listed on IDEAS

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    Cited by:

    1. Sachin Kumar & Kumari Sarita & Akanksha Singh S Vardhan & Rajvikram Madurai Elavarasan & R. K. Saket & Narottam Das, 2020. "Reliability Assessment of Wind-Solar PV Integrated Distribution System Using Electrical Loss Minimization Technique," Energies, MDPI, vol. 13(21), pages 1-30, October.
    2. Houssem Ben Aribia & Ali M. El-Rifaie & Mohamed A. Tolba & Abdullah Shaheen & Ghareeb Moustafa & Fahmi Elsayed & Mostafa Elshahed, 2023. "Growth Optimizer for Parameter Identification of Solar Photovoltaic Cells and Modules," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    3. 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.
    4. Ines Sansa & Zina Boussaada & Najiba Mrabet Bellaaj, 2021. "Solar Radiation Prediction Using a Novel Hybrid Model of ARMA and NARX," Energies, MDPI, vol. 14(21), pages 1-26, October.

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