IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i19p10575-d641718.html
   My bibliography  Save this article

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

Author

Listed:
  • Amjad Ali

    (Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS), King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Kashif Irshad

    (Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS), King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Mohammad Farhan Khan

    (School of Water, Energy and Environment, Cranfield University, Bedford MK43 0AL, UK)

  • Md Moinul Hossain

    (School of Engineering, University of Kent, Canterbury CT2 7NT, UK)

  • Ibrahim N. A. Al-Duais

    (Civil & Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Muhammad Zeeshan Malik

    (School of Electronics and Information Engineering, Taizhou University, Taizhou 318000, China)

Abstract

Substantial progress in solar photovoltaic (SPV) dissemination in grid-connected and standalone power generation systems has been witnessed during the last two decades. However, weather intermittency has a non-linear characteristic impact on solar photovoltaic output, which can cause considerable loss in the system’s overall output. To overcome these inevitable losses and optimize the SPV output, maximum power point tracking (MPPT) is mounted in the middle of the power electronics converters and SPV to achieve the maximum output with better precision from the SPV system under intermittent weather conditions. As MPPT is considered an essential part of the SPV system, up to now, many researchers have developed numerous MPPT techniques, each with unique features. A Google Scholar survey from 2015–2021 was performed to scrutinize the number of published review papers in this area. An online search established that on different MPPT techniques, overall, 100 review articles were published; out of these 100, seven reviews on conventional MPPT techniques under shading or partial shading and only four under non-uniform solar irradiance are published. Unfortunately, no dedicated review article has explicitly focused on soft computing MPPT (SC-MPPT) techniques. Therefore, a comprehensive review of articles on SC-MPPT techniques is desirable, in which almost all the familiar SC-MPPT techniques have to be summarized in one piece. This review article concentrates explicitly on soft computing-based MPPT techniques under non-uniform irradiance conditions along with their operating principles, block/flow diagram. It will not only be helpful for academics and researchers to provide a future direction in SC-MPPT optimization research, but also help the field engineers to select the appropriate SC-MPPT for SPV according to system design and environmental conditions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10575-:d:641718
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/19/10575/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/19/10575/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nguyen, Thang Trung & Vo, Dieu Ngoc & Truong, Anh Viet, 2014. "Cuckoo search algorithm for short-term hydrothermal scheduling," Applied Energy, Elsevier, vol. 132(C), pages 276-287.
    2. Ramli, Makbul A.M. & Twaha, Ssennoga & Ishaque, Kashif & Al-Turki, Yusuf A., 2017. "A review on maximum power point tracking for photovoltaic systems with and without shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 144-159.
    3. Liu, Liqun & Meng, Xiaoli & Liu, Chunxia, 2016. "A review of maximum power point tracking methods of PV power system at uniform and partial shading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1500-1507.
    4. Larbes, C. & Aït Cheikh, S.M. & Obeidi, T. & Zerguerras, A., 2009. "Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system," Renewable Energy, Elsevier, vol. 34(10), pages 2093-2100.
    5. Katalin Bódis & Ioannis Kougias & Nigel Taylor & Arnulf Jäger-Waldau, 2019. "Solar Photovoltaic Electricity Generation: A Lifeline for the European Coal Regions in Transition," Sustainability, MDPI, vol. 11(13), pages 1-14, July.
    6. 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.
    7. Héctor Marañón-Ledesma & Asgeir Tomasgard, 2019. "Analyzing Demand Response in a Dynamic Capacity Expansion Model for the European Power Market," Energies, MDPI, vol. 12(15), pages 1-24, August.
    8. 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.
    9. Victor Andrean & Pei Cheng Chang & Kuo Lung Lian, 2018. "A Review and New Problems Discovery of Four Simple Decentralized Maximum Power Point Tracking Algorithms—Perturb and Observe, Incremental Conductance, Golden Section Search, and Newton’s Quadratic Int," Energies, MDPI, vol. 11(11), pages 1-25, November.
    10. Li, Guiqiang & Jin, Yi & Akram, M.W. & Chen, Xiao & Ji, Jie, 2018. "Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 840-873.
    11. 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.
    12. 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.
    13. Ustun, Taha Selim & Ozansoy, Cagil & Zayegh, Aladin, 2011. "Recent developments in microgrids and example cases around the world—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 4030-4041.
    14. 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.
    15. 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.
    16. 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.
    17. Hina Gohar Ali & Ramon Vilanova Arbos, 2020. "Chattering Free Adaptive Sliding Mode Controller for Photovoltaic Panels with Maximum Power Point Tracking," Energies, MDPI, vol. 13(21), pages 1-18, October.
    18. 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.
    19. Hossain, M.Z. & Rahim, N.A. & Selvaraj, Jeyraj a/l, 2018. "Recent progress and development on power DC-DC converter topology, control, design and applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 205-230.
    20. Mellit, A. & Benghanem, M. & Kalogirou, S.A., 2007. "Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure," Renewable Energy, Elsevier, vol. 32(2), pages 285-313.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohamed Derbeli & Cristian Napole & Oscar Barambones & Jesus Sanchez & Isidro Calvo & Pablo Fernández-Bustamante, 2021. "Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications," Energies, MDPI, vol. 14(22), pages 1-31, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Belhachat, Faiza & Larbes, Cherif, 2017. "Global maximum power point tracking based on ANFIS approach for PV array configurations under partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 875-889.
    3. 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.
    4. Seyedmahmoudian, M. & Horan, B. & Soon, T. Kok & Rahmani, R. & Than Oo, A. Muang & Mekhilef, S. & Stojcevski, A., 2016. "State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 435-455.
    5. 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.
    6. Kermadi, Mostefa & Berkouk, El Madjid, 2017. "Artificial intelligence-based maximum power point tracking controllers for Photovoltaic systems: Comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 369-386.
    7. Joshi, Puneet & Arora, Sudha, 2017. "Maximum power point tracking methodologies for solar PV systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1154-1177.
    8. Rezk, Hegazy & AL-Oran, Mazen & Gomaa, Mohamed R. & Tolba, Mohamed A. & Fathy, Ahmed & Abdelkareem, Mohammad Ali & Olabi, A.G. & El-Sayed, Abou Hashema M., 2019. "A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    9. Ramli, Makbul A.M. & Twaha, Ssennoga & Ishaque, Kashif & Al-Turki, Yusuf A., 2017. "A review on maximum power point tracking for photovoltaic systems with and without shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 144-159.
    10. 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.
    11. Osmani, Khaled & Haddad, Ahmad & Lemenand, Thierry & Castanier, Bruno & Ramadan, Mohamad, 2021. "An investigation on maximum power extraction algorithms from PV systems with corresponding DC-DC converters," Energy, Elsevier, vol. 224(C).
    12. Rizzo, Santi Agatino & Scelba, Giacomo, 2015. "ANN based MPPT method for rapidly variable shading conditions," Applied Energy, Elsevier, vol. 145(C), pages 124-132.
    13. 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.
    14. Mellit, Adel & Kalogirou, Soteris A., 2014. "MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives," Energy, Elsevier, vol. 70(C), pages 1-21.
    15. Das, Soubhagya K. & Verma, Deepak & Nema, Savita & Nema, R.K., 2017. "Shading mitigation techniques: State-of-the-art in photovoltaic applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 369-390.
    16. 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.
    17. Mohapatra, Alivarani & Nayak, Byamakesh & Das, Priti & Mohanty, Kanungo Barada, 2017. "A review on MPPT techniques of PV system under partial shading condition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 854-867.
    18. 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.
    19. Tingting Pei & Xiaohong Hao & Qun Gu, 2018. "A Novel Global Maximum Power Point Tracking Strategy Based on Modified Flower Pollination Algorithm for Photovoltaic Systems under Non-Uniform Irradiation and Temperature Conditions," Energies, MDPI, vol. 11(10), pages 1-16, October.
    20. Rajesh, R. & Carolin Mabel, M., 2015. "A comprehensive review of photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 231-248.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10575-:d:641718. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.