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An Optimized PV Control System Based on the Emperor Penguin Optimizer

Author

Listed:
  • Mariam A. Sameh

    (Electrical Engineering Department, Future University in Egypt, Cairo 11769, Egypt)

  • Mostafa I. Marei

    (Electrical Power and Machines Department, Ain Shams University, Cairo 11769, Egypt)

  • M. A. Badr

    (Electrical Engineering Department, Future University in Egypt, Cairo 11769, Egypt)

  • Mahmoud A. Attia

    (Electrical Power and Machines Department, Ain Shams University, Cairo 11769, Egypt)

Abstract

During the day, photovoltaic (PV) systems are exposed to different sunlight conditions in addition to partial shading (PS). Accordingly, maximum power point tracking (MPPT) techniques have become essential for PV systems to secure harvesting the maximum possible power from the PV modules. In this paper, optimized control is performed through the application of relatively newly developed optimization algorithms to PV systems under Partial Shading (PS) conditions. The initial value of the duty cycle of the boost converter is optimized for maximizing the amount of power extracted from the PV arrays. The emperor penguin optimizer (EPO) is proposed not only to optimize the initial setting of duty cycle but to tune the gains of controllers used for the boost converter and the grid-connected inverter of the PV system. In addition, the performance of the proposed system based on the EPO algorithm is compared with another newly developed optimization technique based on the cuttlefish algorithm (CFA). Moreover, particle swarm optimization (PSO) algorithm is used as a reference algorithm to compare results with both EPO and CFA. PSO is chosen since it is an old, well-tested, and effective algorithm. For the evaluation of performance of the proposed PV system using the proposed algorithms under different PS conditions, results are recorded and introduced.

Suggested Citation

  • Mariam A. Sameh & Mostafa I. Marei & M. A. Badr & Mahmoud A. Attia, 2021. "An Optimized PV Control System Based on the Emperor Penguin Optimizer," Energies, MDPI, vol. 14(3), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:751-:d:490686
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    References listed on IDEAS

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    1. Yu-Pei Huang & Cheng-En Ye & Xiang Chen, 2018. "A Modified Firefly Algorithm with Rapid Response Maximum Power Point Tracking for Photovoltaic Systems under Partial Shading Conditions," Energies, MDPI, vol. 11(9), pages 1-33, August.
    2. 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.
    3. Ram, J.Prasanth & Rajasekar, N. & Miyatake, Masafumi, 2017. "Design and overview of maximum power point tracking techniques in wind and solar photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1138-1159.
    4. Mohammadmehdi Seyedmahmoudian & Saad Mekhilef & Rasoul Rahmani & Rubiyah Yusof & Ehsan Taslimi Renani, 2013. "Analytical Modeling of Partially Shaded Photovoltaic Systems," Energies, MDPI, vol. 6(1), pages 1-17, January.
    5. Neeraj Priyadarshi & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Frede Blaabjerg & Farooque Azam, 2018. "Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm," Energies, MDPI, vol. 11(5), pages 1-16, April.
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    Cited by:

    1. Cătălin Alexandru, 2023. "PV Tracking Systems," Energies, MDPI, vol. 16(6), pages 1-3, March.
    2. Saleh Masoud Abdallah Altbawi & Ahmad Safawi Bin Mokhtar & Saifulnizam Bin Abdul Khalid & Nusrat Husain & Ashraf Yahya & Syed Aqeel Haider & Rayan Hamza Alsisi & Lubna Moin, 2023. "Optimal Control of a Single-Stage Modular PV-Grid-Driven System Using a Gradient Optimization Algorithm," Energies, MDPI, vol. 16(3), pages 1-23, February.

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