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A critical evaluation on maximum power point tracking methods for partial shading in PV systems

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  • Ahmed, Jubaer
  • Salam, Zainal

Abstract

As photovoltaic (PV) system suffers considerable energy loss due to partial shading (PS), various approaches have been proposed to mitigate this problem. Among these, improving the maximum power point tracker (MPPT) algorithm seems to be the most feasible and economical solution. To date, there appears to be an absence of a single review paper that critically evaluates the performance of various types of MPPT algorithms during PS. To fill this gap, fifty prominent works on PS are analyzed from the theoretical and operational point of view. In particular, the paper will closely address the accurate detection for PS occurrence and the efficiency of global peak tracking. For certain selected cases, in-depth analysis is carried out to allow for an improved understanding on the operational intricacies of the algorithm. It is envisaged that this review paper would be a valuable one-stop reference to enable PV professionals to make more informed decisions when designing or choosing new MPPT algorithms for their inverters.

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  • Ahmed, Jubaer & Salam, Zainal, 2015. "A critical evaluation on maximum power point tracking methods for partial shading in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 933-953.
  • Handle: RePEc:eee:rensus:v:47:y:2015:i:c:p:933-953
    DOI: 10.1016/j.rser.2015.03.080
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    1. Daraban, Stefan & Petreus, Dorin & Morel, Cristina, 2014. "A novel MPPT (maximum power point tracking) algorithm based on a modified genetic algorithm specialized on tracking the global maximum power point in photovoltaic systems affected by partial shading," Energy, Elsevier, vol. 74(C), pages 374-388.
    2. Di Piazza, Maria Carmela & Vitale, Gianpaolo, 2010. "Photovoltaic field emulation including dynamic and partial shadow conditions," Applied Energy, Elsevier, vol. 87(3), pages 814-823, March.
    3. Ishaque, Kashif & Salam, Zainal, 2013. "A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 475-488.
    4. Ishaque, Kashif & Salam, Zainal & Shamsudin, Amir & Amjad, Muhammad, 2012. "A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm," Applied Energy, Elsevier, vol. 99(C), pages 414-422.
    5. Salam, Zainal & Ahmed, Jubaer & Merugu, Benny S., 2013. "The application of soft computing methods for MPPT of PV system: A technological and status review," Applied Energy, Elsevier, vol. 107(C), pages 135-148.
    6. Kim, Il-Song, 2006. "Sliding mode controller for the single-phase grid-connected photovoltaic system," Applied Energy, Elsevier, vol. 83(10), pages 1101-1115, October.
    7. Liu, Yi-Hua & Chen, Jing-Hsiao & Huang, Jia-Wei, 2015. "A review of maximum power point tracking techniques for use in partially shaded conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 436-453.
    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. Punitha, K. & Devaraj, D. & Sakthivel, S., 2013. "Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions," Energy, Elsevier, vol. 62(C), pages 330-340.
    10. Lin, Chia-Hung & Huang, Cong-Hui & Du, Yi-Chun & Chen, Jian-Liung, 2011. "Maximum photovoltaic power tracking for the PV array using the fractional-order incremental conductance method," Applied Energy, Elsevier, vol. 88(12), pages 4840-4847.
    11. 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.
    12. Bhatnagar, Pallavee & Nema, R.K., 2013. "Maximum power point tracking control techniques: State-of-the-art in photovoltaic applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 224-241.
    13. Wang, Yaw-Juen & Hsu, Po-Chun, 2011. "An investigation on partial shading of PV modules with different connection configurations of PV cells," Energy, Elsevier, vol. 36(5), pages 3069-3078.
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    3. Amjad Ali & Kashif Irshad & Mohammad Farhan Khan & Md Moinul Hossain & Ibrahim N. A. Al-Duais & Muhammad Zeeshan Malik, 2021. "Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review," Sustainability, MDPI, vol. 13(19), pages 1-26, September.
    4. Astitva Kumar & Mohammad Rizwan & Uma Nangia & Muhannad Alaraj, 2021. "Grey Wolf Optimizer-Based Array Reconfiguration to Enhance Power Production from Solar Photovoltaic Plants under Different Scenarios," Sustainability, MDPI, vol. 13(24), pages 1-18, December.
    5. Saba Gul & Azhar Ul Haq & Marium Jalal & Almas Anjum & Ihsan Ullah Khalil, 2019. "A Unified Approach for Analysis of Faults in Different Configurations of PV Arrays and Its Impact on Power Grid," Energies, MDPI, vol. 13(1), pages 1-23, December.
    6. Alfredo Gil-Velasco & Carlos Aguilar-Castillo, 2021. "A Modification of the Perturb and Observe Method to Improve the Energy Harvesting of PV Systems under Partial Shading Conditions," Energies, MDPI, vol. 14(9), pages 1-12, April.
    7. Çelik, Özgür & Teke, Ahmet & Tan, Adnan, 2018. "Overview of micro-inverters as a challenging technology in photovoltaic applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3191-3206.
    8. 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.
    9. Ahmad, R. & Murtaza, Ali F. & Ahmed Sher, Hadeed & Tabrez Shami, Umar & Olalekan, Saheed, 2017. "An analytical approach to study partial shading effects on PV array supported by literature," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 721-732.
    10. Suliang Ma & Mingxuan Chen & Jianwen Wu & Wenlei Huo & Lian Huang, 2016. "Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems," Energies, MDPI, vol. 9(12), pages 1-24, November.
    11. Shahrooz Hajighorbani & Mohd Amran Mohd Radzi & Mohd Zainal Abidin Ab Kadir & Suhaidi Shafie & Muhammad Ammirrul Atiqi Mohd Zainuri, 2016. "Implementing a Novel Hybrid Maximum Power Point Tracking Technique in DSP via Simulink/MATLAB under Partially Shaded Conditions," Energies, MDPI, vol. 9(2), pages 1-25, January.
    12. T. Nagadurga & P. V. R. L. Narasimham & V. S. Vakula, 2021. "Global Maximum Power Point Tracking of Solar Photovoltaic Strings under Partial Shading Conditions Using Cat Swarm Optimization Technique," Sustainability, MDPI, vol. 13(19), pages 1-20, October.
    13. Balamurugan, M. & Sahoo, Sarat Kumar & Sukchai, Sukruedee, 2017. "Application of soft computing methods for grid connected PV system: A technological and status review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1493-1508.
    14. Kamran Ali Khan Niazi & Yongheng Yang & Mashood Nasir & Dezso Sera, 2019. "Evaluation of Interconnection Configuration Schemes for PV Modules with Switched-Inductor Converters under Partial Shading Conditions," Energies, MDPI, vol. 12(14), pages 1-12, July.
    15. Abu Eldahab, Yasser E. & Saad, Naggar H. & Zekry, Abdalhalim, 2016. "Enhancing the design of battery charging controllers for photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 646-655.
    16. Belhaouas, N. & Cheikh, M.-S. Ait & Agathoklis, P. & Oularbi, M.-R. & Amrouche, B. & Sedraoui, K. & Djilali, N., 2017. "PV array power output maximization under partial shading using new shifted PV array arrangements," Applied Energy, Elsevier, vol. 187(C), pages 326-337.
    17. Chettibi, N. & Mellit, A., 2018. "Intelligent control strategy for a grid connected PV/SOFC/BESS energy generation system," Energy, Elsevier, vol. 147(C), pages 239-262.
    18. Adel O. Baatiah & Ali M. Eltamaly & Majed A. Alotaibi, 2023. "Improving Photovoltaic MPPT Performance through PSO Dynamic Swarm Size Reduction," Energies, MDPI, vol. 16(18), pages 1-15, September.
    19. Eltamaly, Ali M. & Al-Saud, M.S. & Abokhalil, Ahmed G. & Farh, Hassan M.H., 2020. "Simulation and experimental validation of fast adaptive particle swarm optimization strategy for photovoltaic global peak tracker under dynamic partial shading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    20. Azhar Ul-Haq & Shah Fahad & Saba Gul & Rui Bo, 2023. "Intelligent Control Schemes for Maximum Power Extraction from Photovoltaic Arrays under Faults," Energies, MDPI, vol. 16(2), pages 1-24, January.
    21. Bradai, R. & Boukenoui, R. & Kheldoun, A. & Salhi, H. & Ghanes, M. & Barbot, J-P. & Mellit, A., 2017. "Experimental assessment of new fast MPPT algorithm for PV systems under non-uniform irradiance conditions," Applied Energy, Elsevier, vol. 199(C), pages 416-429.
    22. Saud Alotaibi & Ahmed Darwish, 2021. "Modular Multilevel Converters for Large-Scale Grid-Connected Photovoltaic Systems: A Review," Energies, MDPI, vol. 14(19), pages 1-30, September.
    23. Abu Eldahab, Yasser E. & Saad, Naggar H. & Zekry, Abdalhalim, 2017. "Enhancing the tracking techniques for the global maximum power point under partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1173-1183.
    24. Amjad Ali & K. Almutairi & Muhammad Zeeshan Malik & Kashif Irshad & Vineet Tirth & Salem Algarni & Md. Hasan Zahir & Saiful Islam & Md Shafiullah & Neeraj Kumar Shukla, 2020. "Review of Online and Soft Computing Maximum Power Point Tracking Techniques under Non-Uniform Solar Irradiation Conditions," Energies, MDPI, vol. 13(12), pages 1-37, June.

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