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Systematic Literature Review and Benchmarking for Photovoltaic MPPT Techniques

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Listed:
  • Hsen Abidi

    (Laboratory of Energy Applications and Renewable Energy Efficiency (LAPER), Faculty of Sciences of Tunis, El Manar University, Tunis 1068, Tunisia)

  • Lilia Sidhom

    (Laboratory of Energy Applications and Renewable Energy Efficiency (LAPER), Faculty of Sciences of Tunis, El Manar University, Tunis 1068, Tunisia
    Mechanical Department, National Engineering School of Bizerte, University of Carthage, Amilcar 1054, Tunisia)

  • Ines Chihi

    (Department of Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, Campus Kirchberg, 1359 Luxembourg, Luxembourg)

Abstract

There are a variety of maximum power point tracking (MPPT) algorithms for improving the energy efficiency of solar photovoltaic (PV) systems. The mode of implementation (digital or analog), design simplicity, sensor requirements, convergence speed, range of efficacy, and hardware costs are the primary distinctions between these algorithms. Selecting an appropriate algorithm is critical for users, as it influences the electrical efficiency of PV systems and lowers costs by reducing the number of solar panels required to achieve the desired output. This research is relevant since PV systems are an alternative and sustainable solution for energy production. The main aim of this paper is to review the current advances in MPPT algorithms. This paper first undertakes a systematic literature review (SLR) of various MPPT algorithms, highlighting their strengths and weaknesses; a detailed summary of the related reviews on this topic is then presented. Next, quantitative and qualitative comparisons of the most popular and efficient MPPT methods are performed. This comparison is based on simulation results to provide efficient benchmarking of MPPT algorithms. This benchmarking validates that intelligent MPPTs, such as artificial neural network (ANN), fuzzy logic control (FLC), and adaptive neuro-fuzzy inference system (ANFIS), outperform other approaches in tracking the MPPT of PV systems. Specifically, the ANN technique had the highest efficiency of 98.6%, while the ANFIS and FLC methods were close behind with efficiencies of 98.34% and 98.29%, respectively. Therefore, it is recommended that these intelligent MPPT techniques be considered for use in future photovoltaic systems to achieve optimal power output and maximize energy production.

Suggested Citation

  • Hsen Abidi & Lilia Sidhom & Ines Chihi, 2023. "Systematic Literature Review and Benchmarking for Photovoltaic MPPT Techniques," Energies, MDPI, vol. 16(8), pages 1-45, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3509-:d:1126248
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    References listed on IDEAS

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    1. Reshma Gopi, R. & Sreejith, S., 2018. "Converter topologies in photovoltaic applications – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 1-14.
    2. Abhishek Sharma & Abhinav Sharma & Vibhu Jately & Moshe Averbukh & Shailendra Rajput & Brian Azzopardi, 2022. "A Novel TSA-PSO Based Hybrid Algorithm for GMPP Tracking under Partial Shading Conditions," Energies, MDPI, vol. 15(9), pages 1-21, April.
    3. Haidar Islam & Saad Mekhilef & Noraisyah Binti Mohamed Shah & Tey Kok Soon & Mehdi Seyedmahmousian & Ben Horan & Alex Stojcevski, 2018. "Performance Evaluation of Maximum Power Point Tracking Approaches and Photovoltaic Systems," Energies, MDPI, vol. 11(2), pages 1-24, February.
    4. Srete Nikolovski & Hamid Reza Baghaee & Dragan Mlakić, 2018. "ANFIS-Based Peak Power Shaving/Curtailment in Microgrids Including PV Units and BESSs," Energies, MDPI, vol. 11(11), pages 1-23, October.
    5. Danandeh, M.A. & Mousavi G., S.M., 2018. "Comparative and comprehensive review of maximum power point tracking methods for PV cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2743-2767.
    6. Fahd A. Alturki & Abdullrahman A. Al-Shamma’a & Hassan M. H. Farh, 2020. "Simulations and dSPACE Real-Time Implementation of Photovoltaic Global Maximum Power Extraction under Partial Shading," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    7. Li, Xingshuo & Wen, Huiqing & Hu, Yihua & Jiang, Lin, 2019. "A novel beta parameter based fuzzy-logic controller for photovoltaic MPPT application," Renewable Energy, Elsevier, vol. 130(C), pages 416-427.
    8. Sundareswaran, K. & Vignesh kumar, V. & Palani, S., 2015. "Application of a combined particle swarm optimization and perturb and observe method for MPPT in PV systems under partial shading conditions," Renewable Energy, Elsevier, vol. 75(C), pages 308-317.
    9. Mingrui Zhang & Zheyang Chen & Li Wei, 2019. "An Immune Firefly Algorithm for Tracking the Maximum Power Point of PV Array under Partial Shading Conditions," Energies, MDPI, vol. 12(16), pages 1-15, August.
    10. Ahmed, Jubaer & Salam, Zainal, 2015. "An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency," Applied Energy, Elsevier, vol. 150(C), pages 97-108.
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