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Design of an Efficient MPPT Topology Based on a Grey Wolf Optimizer-Particle Swarm Optimization (GWO-PSO) Algorithm for a Grid-Tied Solar Inverter Under Variable Rapid-Change Irradiance

Author

Listed:
  • Salah Abbas Taha

    (Electrical Engineering Technical College, Middle Technical University, Baghdad 10074, Iraq)

  • Zuhair S. Al-Sagar

    (Department of Renewable Energy Techniques, Middle Technical University, Baghdad 10074, Iraq)

  • Mohammed Abdulla Abdulsada

    (Electrical Engineering Technical College, Middle Technical University, Baghdad 10074, Iraq)

  • Mohammed Alruwaili

    (Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia)

  • Moustafa Ahmed Ibrahim

    (Electrical Engineering Department, University of Business and Technology, Jeddah 23435, Saudi Arabia)

Abstract

A grid-tied inverter needs excellent maximum power point tracking (MPPT) topology to extract the maximum energy from PV panels regarding energy creation. An efficient MPPT ensures that grid codes are met, maintains power quality and system reliability, minimizes power losses, and suppresses rapid response to power fluctuations due to solar irradiance. Moreover, appropriate MPPT enhances economic returns by increasing energy royalties and ensures high power quality with reduced harmonic distortion. For these reasons, an improved hybrid MPPT technique for a grid-tied solar system is presented based on particle swarm optimization (PSO) and grey wolf optimizer (GWO-PSO) to achieve these objectives. The proposed method is tested under MATLAB/Simulink 2024a for a 100 kW PV array connected with a boost converter to link with a voltage source converter (VSC). The simulation results show that the proposed GWO-PSO can reduce the overshoot on rise time along with settling time, meaning less time is wasted within the grid power system. Moreover, the suggested method is compared with PSO, GWO, and horse herd optimization (HHO) under different weather conditions. The results show that the other algorithms respond more slowly and exhibit higher overshoot, which can be counterproductive. These comparisons validate the proposed method as more accurate, demonstrating that it can enhance the real power quality that is transferred to the grid.

Suggested Citation

  • Salah Abbas Taha & Zuhair S. Al-Sagar & Mohammed Abdulla Abdulsada & Mohammed Alruwaili & Moustafa Ahmed Ibrahim, 2025. "Design of an Efficient MPPT Topology Based on a Grey Wolf Optimizer-Particle Swarm Optimization (GWO-PSO) Algorithm for a Grid-Tied Solar Inverter Under Variable Rapid-Change Irradiance," Energies, MDPI, vol. 18(8), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:1997-:d:1633684
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    References listed on IDEAS

    as
    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. Refaat, Ahmed & Ali, Qays Adnan & Elsakka, Mohamed Mohamed & Elhenawy, Yasser & Majozi, Thokozani & Korovkin, Nikolay V. & Elfar, Medhat Hegazy, 2024. "Extraction of maximum power from PV system based on horse herd optimization MPPT technique under various weather conditions," Renewable Energy, Elsevier, vol. 220(C).
    3. Dilip Kumar & Yogesh Kumar Chauhan & Ajay Shekhar Pandey & Ankit Kumar Srivastava & Varun Kumar & Faisal Alsaif & Rajvikram Madurai Elavarasan & Md Rabiul Islam & Raju Kannadasan & Mohammed H. Alshari, 2023. "A Novel Hybrid MPPT Approach for Solar PV Systems Using Particle-Swarm-Optimization-Trained Machine Learning and Flying Squirrel Search Optimization," Sustainability, MDPI, vol. 15(6), pages 1-29, March.
    Full references (including those not matched with items on IDEAS)

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