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Jellyfish Search Optimization Algorithm for MPP Tracking of PV System

Author

Listed:
  • Afroz Alam

    (Department of Electrical Engineering ZHCET, Aligarh Muslim University, Aligarh 202002, India)

  • Preeti Verma

    (Department of Electrical Engineering ZHCET, Aligarh Muslim University, Aligarh 202002, India)

  • Mohd Tariq

    (Department of Electrical Engineering ZHCET, Aligarh Muslim University, Aligarh 202002, India)

  • Adil Sarwar

    (Department of Electrical Engineering ZHCET, Aligarh Muslim University, Aligarh 202002, India)

  • Basem Alamri

    (Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia)

  • Noore Zahra

    (Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia)

  • Shabana Urooj

    (Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh 84428, Saudi Arabia)

Abstract

Because of the rapid increase in the depletion rate of conventional energy sources, the energy crisis has become a central problem in the contemporary world. This issue opens the gateway for exploring and developing renewable energy sources to fulfill the exigent energy demand. Solar energy is an abundant source of sustainable energy and hence, nowadays, solar photovoltaic (PV) systems are employed to extract energy from solar irradiation. However, the PV systems need to work at the maximum power point (MPP) to exploit the highest accessible power during varying operating conditions. For this reason, maximum power point tracking (MPPT) algorithms are used to track the optimum power point. Furthermore, the efficient utilization of PV systems is hindered by renowned partial shading conditions (PSC), which generate multiple peaks in the power-voltage characteristic of the PV array. Thus, this article addresses the performance of the newly developed jellyfish search optimization (JSO) strategy in the PV frameworks to follow the global maximum power point (GMPP) under PSC.

Suggested Citation

  • Afroz Alam & Preeti Verma & Mohd Tariq & Adil Sarwar & Basem Alamri & Noore Zahra & Shabana Urooj, 2021. "Jellyfish Search Optimization Algorithm for MPP Tracking of PV System," Sustainability, MDPI, vol. 13(21), pages 1-20, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:11736-:d:663512
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    References listed on IDEAS

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    1. Mohamed Abdel-Basset & Reda Mohamed & Ripon K. Chakrabortty & Michael J. Ryan & Attia El-Fergany, 2021. "An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models," Energies, MDPI, vol. 14(7), pages 1-33, March.
    2. Saravanan, S. & Ramesh Babu, N., 2016. "Maximum power point tracking algorithms for photovoltaic system – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 192-204.
    3. CH Hussaian Basha & C Rani, 2020. "Different Conventional and Soft Computing MPPT Techniques for Solar PV Systems with High Step-Up Boost Converters: A Comprehensive Analysis," Energies, MDPI, vol. 13(2), pages 1-27, January.
    4. Mohd Nadhir Ab Wahab & Samia Nefti-Meziani & Adham Atyabi, 2015. "A Comprehensive Review of Swarm Optimization Algorithms," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-36, May.
    5. Verma, Deepak & Nema, Savita & Shandilya, A.M. & Dash, Soubhagya K., 2016. "Maximum power point tracking (MPPT) techniques: Recapitulation in solar photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1018-1034.
    6. Karami, Nabil & Moubayed, Nazih & Outbib, Rachid, 2017. "General review and classification of different MPPT Techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 1-18.
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    Cited by:

    1. Abdullah Shaheen & Ragab El-Sehiemy & Salah Kamel & Ali Selim, 2022. "Optimal Operational Reliability and Reconfiguration of Electrical Distribution Network Based on Jellyfish Search Algorithm," Energies, MDPI, vol. 15(19), pages 1-14, September.
    2. Ahmed Hussain Elmetwaly & Ramy Adel Younis & Abdelazeem Abdallah Abdelsalam & Ahmed Ibrahim Omar & Mohamed Metwally Mahmoud & Faisal Alsaif & Adel El-Shahat & Mohamed Attya Saad, 2023. "Modeling, Simulation, and Experimental Validation of a Novel MPPT for Hybrid Renewable Sources Integrated with UPQC: An Application of Jellyfish Search Optimizer," Sustainability, MDPI, vol. 15(6), pages 1-30, March.
    3. Waqas Anjum & Abdul Rashid Husain & Junaidi Abdul Aziz & Syed Muhammad Fasih ur Rehman & Muhammad Paend Bakht & Hasan Alqaraghuli, 2022. "A Robust Dynamic Control Strategy for Standalone PV System under Variable Load and Environmental Conditions," Sustainability, MDPI, vol. 14(8), pages 1-27, April.

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