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An Advanced and Robust Approach to Maximize Solar Photovoltaic Power Production

Author

Listed:
  • Muhannad Alaraj

    (Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah 52571, Saudi Arabia)

  • Astitva Kumar

    (Department of Electrical Engineering, Delhi Technological University, Delhi 110042, India)

  • Ibrahim Alsaidan

    (Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah 52571, Saudi Arabia)

  • Mohammad Rizwan

    (Department of Electrical Engineering, Delhi Technological University, Delhi 110042, India)

  • Majid Jamil

    (Department of Electrical Engineering, Jamia Millia Islamia University, Delhi 110025, India)

Abstract

The stochastic and erratic behavior of solar photovoltaic (SPV) is a challenge, especially due to changing meteorological conditions. During a partially irradiated SPV system, the performance of traditional maximum power point tracking (MPPT) controllers is unsatisfactory because of multiple peaks in the Power-Voltage curve. This work is an attempt to understand the performance uncertainties of the SPV system under different shading conditions and its mitigation. Here, a novel hybrid metaheuristic algorithm is proposed for the effective and efficient tracking of power. The algorithm is inspired by the movement of grey wolves and the swarming action of birds, and is thus known as the hybrid grey wolf optimizer (HGWO). The study focuses on the transient and steady-state performance of the proposed controller during different conditions. A comparative analysis of the proposed technique with incremental conductance and a particle swarm optimizer for different configurations is presented. Thus, the results are presented based on power extracted, shading loss, convergence factor and efficiency. The proposed HGWO–MPPT is found to be better as it has a maximum efficiency of 94.30% and a minimum convergence factor of 0.20 when compared with other techniques under varying conditions for different topologies. Furthermore, a practical assessment of the proposed controller on a 6.3 kW p rooftop SPV system is also presented in the paper. Energy production is increased by 8.55% using the proposed approach to the practical system.

Suggested Citation

  • Muhannad Alaraj & Astitva Kumar & Ibrahim Alsaidan & Mohammad Rizwan & Majid Jamil, 2022. "An Advanced and Robust Approach to Maximize Solar Photovoltaic Power Production," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7398-:d:840735
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    References listed on IDEAS

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    1. Ram, J. Prasanth & Babu, T. Sudhakar & Rajasekar, N., 2017. "A comprehensive review on solar PV maximum power point tracking techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 826-847.
    2. Astitva Kumar & Mohammad Rizwan & Uma Nangia & Muhannad Alaraj, 2021. "Grey Wolf Optimizer-Based Array Reconfiguration to Enhance Power Production from Solar Photovoltaic Plants under Different Scenarios," Sustainability, MDPI, vol. 13(24), pages 1-18, December.
    3. Ali M. Eltamaly, 2021. "A Novel Strategy for Optimal PSO Control Parameters Determination for PV Energy Systems," Sustainability, MDPI, vol. 13(2), pages 1-28, January.
    4. Eltamaly, Ali M., 2021. "A novel musical chairs algorithm applied for MPPT of PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    5. Ali M. Eltamaly, 2021. "An Improved Cuckoo Search Algorithm for Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions," Energies, MDPI, vol. 14(4), pages 1-26, February.
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    Cited by:

    1. Amit Kumar Sharma & Rupendra Kumar Pachauri & Sushabhan Choudhury & Ahmad Faiz Minai & Majed A. Alotaibi & Hasmat Malik & Fausto Pedro García Márquez, 2023. "Role of Metaheuristic Approaches for Implementation of Integrated MPPT-PV Systems: A Comprehensive Study," Mathematics, MDPI, vol. 11(2), pages 1-48, January.

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