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Modular Level Power Electronics (MLPE) Based Distributed PV System for Partial Shaded Conditions

Author

Listed:
  • Sajid Sarwar

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan)

  • Muhammad Yaqoob Javed

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan)

  • Mujtaba Hussain Jaffery

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan)

  • Muhammad Saqib Ashraf

    (Department of Electrical Engineering, Government College University, Faisalabad 38000, Pakistan)

  • Muhammad Talha Naveed

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan)

  • Muhammad Annas Hafeez

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan)

Abstract

Photovoltaic (PV) solar energy is a very promising renewable energy technology, as solar PV systems are less efficient because of climate conditions, temperature, and irradiance change. So, to resolve this problem, two PV topologies are used, i.e., centralized and distributed PV systems. The centralized technique is quicker than the distributed technique in terms of convergence speed and a faster power tracking approach. In the event of uniform irradiance, the centralized system also has the benefit of supplying superior energy, but in PS scenarios, a huge amount of energy is lost. However, the distributed approach requires current and voltage measurements at each panel, resulting in a massive data set. Nevertheless, in the event of shading circumstances, the distributed technique is highly effective because a modular level power electronics (MLPE) converter is used. While in a centralized PV system, there is only a single DC-DC converter for the whole PV system. In this research work, a DFO-based DC-DC converter is designed for modular level, with an ability to perform a rapid shutdown of the module under fire hazard conditions, troubleshooting, and monitoring of a module in a very efficient way. The robustness of the proposed MPPT DFO algorithm is tested with different techniques such as Cuckoo Search (CS), Fruit Fly Optimization (FFO), Particle swarm optimization (PSO), Incremental conductance (InC), and Perturb and observe(P&O) techniques. The proposed technique shows better results in terms of MPPT efficiency, dynamic responsiveness, and harmonics. Furthermore, the result of MLPE and the centralized system is verified by using the Helioscope with different inverter companies like SMA, Tigo, Enphase, Solar edge, and Huawei. The results prove that MLPE is a better option in the case of shading region for attaining the maximum power point.

Suggested Citation

  • Sajid Sarwar & Muhammad Yaqoob Javed & Mujtaba Hussain Jaffery & Muhammad Saqib Ashraf & Muhammad Talha Naveed & Muhammad Annas Hafeez, 2022. "Modular Level Power Electronics (MLPE) Based Distributed PV System for Partial Shaded Conditions," Energies, MDPI, vol. 15(13), pages 1-39, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4797-:d:852548
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    References listed on IDEAS

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    Cited by:

    1. Lilia Tightiz & Saeedeh Mansouri & Farhad Zishan & Joon Yoo & Nima Shafaghatian, 2022. "Maximum Power Point Tracking for Photovoltaic Systems Operating under Partially Shaded Conditions Using SALP Swarm Algorithm," Energies, MDPI, vol. 15(21), pages 1-17, November.
    2. Ngoc Thien Le & Thanh Le Truong & Widhyakorn Asdornwised & Surachai Chaitusaney & Watit Benjapolakul, 2023. "Energy Production Analysis of Rooftop PV Systems Equipped with Module-Level Power Electronics under Partial Shading Conditions Based on Mixed-Effects Model," Energies, MDPI, vol. 16(2), pages 1-15, January.
    3. Galal Al-Muthanna & Shuhua Fang & Ibrahim AL-Wesabi & Khaled Ameur & Hossam Kotb & Kareem M. AboRas & Hassan Z. Al Garni & Abdullahi Abubakar Mas’ud, 2023. "A High Speed MPPT Control Utilizing a Hybrid PSO-PID Controller under Partially Shaded Photovoltaic Battery Chargers," Sustainability, MDPI, vol. 15(4), pages 1-28, February.
    4. Chanuri Charin & Dahaman Ishak & Muhammad Ammirrul Atiqi Mohd Zainuri & Baharuddin Ismail & Turki Alsuwian & Adam R. H. Alhawari, 2022. "Modified Levy-based Particle Swarm Optimization (MLPSO) with Boost Converter for Local and Global Point Tracking," Energies, MDPI, vol. 15(19), pages 1-30, October.

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