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An Enhanced Cuckoo Search Algorithm Fitting for Photovoltaic Systems’ Global Maximum Power Point Tracking under Partial Shading Conditions

Author

Listed:
  • Ehab Mohamed Ali

    (Electrical Engineering Department, Faculty of Engineering, Alexandria University, Alexandria 21511, Egypt
    Egyptian Air Defense Collage, Alexandria University, Alexandria 21511, Egypt)

  • Ahmed K. Abdelsalam

    (Electrical Engineering Department, Arab Academy for Science and Technology & Maritime Transport, Alexandria 21511, Egypt)

  • Karim H. Youssef

    (Electrical Engineering Department, Faculty of Engineering, Alexandria University, Alexandria 21511, Egypt)

  • Ahmed A. Hossam-Eldin

    (Electrical Engineering Department, Faculty of Engineering, Alexandria University, Alexandria 21511, Egypt)

Abstract

The output power against voltage curve of the photovoltaic system changes its characteristics under partial shading conditions because of using bypass diodes. These bypass diodes are connected across the PV modules inside the string to avoid hotspot formation in the shaded PV modules. Therefore, the output curve has multiple power peaks with only one Global Max Power Point. The classical Maximum Power Point Tracking algorithms may fail to track that Global Max Power. Several soft computing algorithms have been proposed to improve tracking efficiency with different optimization principles. In this paper, an Improved Cuckoo Search Algorithm has been proposed to increase the tracking speed with minimum output power oscillation. The proposed algorithm avoids spreading the initial particles among the whole curve to predict shading pattern, but it reduces the exploration area after each iteration to compensate for the algorithm’s randomness. The proposed algorithm was compared with other methods by simulation using MATLAB/Simulink program and with practical experiments under the same operating conditions. The comparison showed that the proposed algorithm overcomes the other methods’ drawbacks and concurrently minimizes the convergence time, power oscillation, and system power losses.

Suggested Citation

  • Ehab Mohamed Ali & Ahmed K. Abdelsalam & Karim H. Youssef & Ahmed A. Hossam-Eldin, 2021. "An Enhanced Cuckoo Search Algorithm Fitting for Photovoltaic Systems’ Global Maximum Power Point Tracking under Partial Shading Conditions," Energies, MDPI, vol. 14(21), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7210-:d:670532
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    References listed on IDEAS

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    1. Muhannad Alshareef & Zhengyu Lin & Mingyao Ma & Wenping Cao, 2019. "Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions," Energies, MDPI, vol. 12(4), pages 1-18, February.
    2. Novie Ayub Windarko & Muhammad Nizar Habibi & Bambang Sumantri & Eka Prasetyono & Moh. Zaenal Efendi & Taufik, 2021. "A New MPPT Algorithm for Photovoltaic Power Generation under Uniform and Partial Shading Conditions," Energies, MDPI, vol. 14(2), pages 1-22, January.
    3. Jirada Gosumbonggot & Goro Fujita, 2019. "Partial Shading Detection and Global Maximum Power Point Tracking Algorithm for Photovoltaic with the Variation of Irradiation and Temperature," Energies, MDPI, vol. 12(2), pages 1-22, January.
    4. Ahmed, Jubaer & Salam, Zainal, 2014. "A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability," Applied Energy, Elsevier, vol. 119(C), pages 118-130.
    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. Muqaddas Elahi & Hafiz Muhammad Ashraf & Chul-Hwan Kim, 2022. "An Improved Partial Shading Detection Strategy Based on Chimp Optimization Algorithm to Find Global Maximum Power Point of Solar Array System," Energies, MDPI, vol. 15(4), pages 1-26, February.
    2. 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.
    3. Zahra Bel Hadj Salah & Saber Krim & Mohamed Ali Hajjaji & Badr M. Alshammari & Khalid Alqunun & Ahmed Alzamil & Tawfik Guesmi, 2023. "A New Efficient Cuckoo Search MPPT Algorithm Based on a Super-Twisting Sliding Mode Controller for Partially Shaded Standalone Photovoltaic System," Sustainability, MDPI, vol. 15(12), pages 1-38, June.
    4. Grzegorz Trzmiel & Jaroslaw Jajczyk & Ewa Kardas-Cinal & Norbert Chamier-Gliszczynski & Waldemar Wozniak & Konrad Lewczuk, 2021. "The Condition of Photovoltaic Modules under Random Operation Parameters," Energies, MDPI, vol. 14(24), pages 1-18, December.

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