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Revolutionizing Photovoltaic Systems: An Innovative Approach to Maximum Power Point Tracking Using Enhanced Dandelion Optimizer in Partial Shading Conditions

Author

Listed:
  • Elmamoune Halassa

    (Applied Automation and Industrial Diagnostic Laboratory (LAADI), Ziane Achour University of Djelfa, Djelfa 17000, Algeria)

  • Lakhdar Mazouz

    (Applied Automation and Industrial Diagnostic Laboratory (LAADI), Ziane Achour University of Djelfa, Djelfa 17000, Algeria)

  • Abdellatif Seghiour

    (Ecole Supérieure en Génie Electrique et Énergétique d’Oran, Laboratory of Electrical and Materials Engineering (LGEM), Oran 31000, Algeria
    Electrical Engineering Laboratory (LGE), University Mohamed Boudiaf of M’sila, BP 166, M’sila 28000, Algeria)

  • Aissa Chouder

    (Electrical Engineering Laboratory (LGE), University Mohamed Boudiaf of M’sila, BP 166, M’sila 28000, Algeria)

  • Santiago Silvestre

    (MNT Group, Electronic Engineering Department, Universitat Politécnica de Catalunya (UPC) BarcelonaTech, C/Jordi Girona 1-3, Campus Nord UPC, 08034 Barcelona, Spain)

Abstract

Partial shading (PS) is a prevalent phenomenon that often affects photovoltaic (PV) installations, leads to the appearance of numerous peaks in the power-voltage characteristics of PV cells, caused by the uneven distribution of solar irradiance on the PV module surface, known as global and local maximum power point (GMPP and LMPP). In this paper, a new technique for achieving GMPP based on the dandelion optimizer (DO) algorithm is proposed, inspired by the movement of dandelion seeds in the wind. The proposed technique aimed to enhance the efficiency of power generation in PV systems, particularly under PS conditions. However, the DO-based MPPT is compared with other advanced maximum power point tracker (MPPT) algorithms, such as Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CSA), and Bat Algorithm (BA). Simulation results establish the superiority and effectiveness of the used MPPT in terms of tracking efficiency, speed, robustness, and simplicity of implementation. Additionally, these results reveal that the DO algorithm exhibits higher performance, with a root mean square error (RMSE) of 1.09 watts, a convergence time of 2.3 milliseconds, and mean absolute error (MAE) of 0.13 watts.

Suggested Citation

  • Elmamoune Halassa & Lakhdar Mazouz & Abdellatif Seghiour & Aissa Chouder & Santiago Silvestre, 2023. "Revolutionizing Photovoltaic Systems: An Innovative Approach to Maximum Power Point Tracking Using Enhanced Dandelion Optimizer in Partial Shading Conditions," Energies, MDPI, vol. 16(9), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3617-:d:1130154
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    References listed on IDEAS

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    1. Eltamaly, Ali M., 2021. "A novel musical chairs algorithm applied for MPPT of PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    2. Bilal Taghezouit & Fouzi Harrou & Cherif Larbes & Ying Sun & Smail Semaoui & Amar Hadj Arab & Salim Bouchakour, 2022. "Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study," Energies, MDPI, vol. 15(21), pages 1-30, October.
    3. Sajid Sarwar & Muhammad Annas Hafeez & Muhammad Yaqoob Javed & Aamer Bilal Asghar & Krzysztof Ejsmont, 2022. "A Horse Herd Optimization Algorithm (HOA)-Based MPPT Technique under Partial and Complex Partial Shading Conditions," Energies, MDPI, vol. 15(5), pages 1-22, March.
    4. Rabeh Abbassi & Salem Saidi & Abdelkader Abbassi & Houssem Jerbi & Mourad Kchaou & Bilal Naji Alhasnawi, 2023. "Accurate Key Parameters Estimation of PEMFCs’ Models Based on Dandelion Optimization Algorithm," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    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:

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