IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i13p7208-d583503.html
   My bibliography  Save this article

Implementation of Different MPPT Techniques in Solar PV Tree under Partial Shading Conditions

Author

Listed:
  • Pitchai Pandiyan

    (Department of EEE, KPR Institute of Engineering and Technology, Coimbatore 641407, India)

  • Subramani Saravanan

    (Department of EEE, Sri Krishna College of Technology, Coimbatore 641042, India)

  • Natarajan Prabaharan

    (Department of EEE, SASTRA Deemed University, Thanjavur 613401, India)

  • Ramji Tiwari

    (Department of EEE, Sri Krishna College of Engineering and Technology, Coimbatore 641008, India)

  • Thangam Chinnadurai

    (Department of ICE, Sri Krishna College of Technology, Coimbatore 641042, India)

  • Neelakandan Ramesh Babu

    (Department of EEE, M.Kumarasamy College of Engineering, Karur 639113, India)

  • Eklas Hossain

    (Department of Electrical Engineering & Renewable Energy, Oregon Institute of Technology, Klamath Falls, OR 97601, USA)

Abstract

This paper presents the design and analytical modeling of the proposed solar photovoltaic standalone system under varying environmental conditions. The proposed system consists of a unique structure of a solar PV-tree, maximum power point tracking (MPPT) technique, and DC–DC converter. The output voltage acquired from the solar PV tree is low. A DC–DC boost converter is utilized to step-up the required amount of voltage level. In this paper, the appropriate duty cycle is obtained for extracting the optimum power from the solar PV tree by using various MPPT mechanisms such as perturb and observe (P&O), incremental conductance (INC), and a radial basis function network (RBFN)-based neural network (NN). The proposed solar photovoltaic tree-based energy harvesting system is designed and validated by using MATLAB/SIMULINK software and real-time application. The simulation results of the above-mentioned three techniques are compared with each other in order to show the effectiveness of the proposed system with RBFN. The RBFN-MPPT provides a significant improvement in tracking efficiency of 6.0% and 5.72% as compared with the P&O method and the INC method at 1000 W/m 2 irradiance condition. From the simulation and real-time results, it is concluded that the RBFN-based NN provides better tracking efficiency and less oscillation as compared with the other two algorithms.

Suggested Citation

  • Pitchai Pandiyan & Subramani Saravanan & Natarajan Prabaharan & Ramji Tiwari & Thangam Chinnadurai & Neelakandan Ramesh Babu & Eklas Hossain, 2021. "Implementation of Different MPPT Techniques in Solar PV Tree under Partial Shading Conditions," Sustainability, MDPI, vol. 13(13), pages 1-22, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7208-:d:583503
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/13/7208/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/13/7208/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Rahate Ahmed & Yeongmin Kim & Zeeshan & Wongee Chun, 2019. "Development of a Tree-Shaped Hybrid Nanogenerator Using Flexible Sheets of Photovoltaic and Piezoelectric Films," Energies, MDPI, vol. 12(2), pages 1-10, January.
    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. Muhammed Y. Worku & Mohamed A. Hassan & Luqman S. Maraaba & Md Shafiullah & Mohamed R. Elkadeem & Md Ismail Hossain & Mohamed A. Abido, 2023. "A Comprehensive Review of Recent Maximum Power Point Tracking Techniques for Photovoltaic Systems under Partial Shading," Sustainability, MDPI, vol. 15(14), pages 1-28, July.
    2. Mohammed Yousri Silaa & Oscar Barambones & José Antonio Cortajarena & Patxi Alkorta & Aissa Bencherif, 2023. "PEMFC Current Control Using a Novel Compound Controller Enhanced by the Black Widow Algorithm: A Comprehensive Simulation Study," Sustainability, MDPI, vol. 15(18), pages 1-23, September.

    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. Cabrera-Tobar, Ana & Bullich-Massagué, Eduard & Aragüés-Peñalba, Mònica & Gomis-Bellmunt, Oriol, 2016. "Review of advanced grid requirements for the integration of large scale photovoltaic power plants in the transmission system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 971-987.
    2. Jiang, Joe-Air & Su, Yu-Li & Kuo, Kun-Chang & Wang, Chien-Hao & Liao, Min-Sheng & Wang, Jen-Cheng & Huang, Chen-Kang & Chou, Cheng-Ying & Lee, Chien-Hsing & Shieh, Jyh-Cherng, 2017. "On a hybrid MPPT control scheme to improve energy harvesting performance of traditional two-stage inverters used in photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1113-1128.
    3. Dhimish, Mahmoud & Holmes, Violeta & Dales, Mark, 2017. "Parallel fault detection algorithm for grid-connected photovoltaic plants," Renewable Energy, Elsevier, vol. 113(C), pages 94-111.
    4. 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.
    5. Sajid Sarwar & Muhammad Yaqoob Javed & Mujtaba Hussain Jaffery & Muhammad Saqib Ashraf & Muhammad Talha Naveed & Muhammad Annas Hafeez, 2022. "Modular Level Power Electronics (MLPE) Based Distributed PV System for Partial Shaded Conditions," Energies, MDPI, vol. 15(13), pages 1-39, June.
    6. Luigi Costanzo & Massimo Vitelli, 2019. "A Novel MPPT Technique for Single Stage Grid-Connected PV Systems: T4S," Energies, MDPI, vol. 12(23), pages 1-13, November.
    7. Sheik Mohammed, S. & Devaraj, D. & Imthias Ahamed, T.P., 2016. "A novel hybrid Maximum Power Point Tracking Technique using Perturb & Observe algorithm and Learning Automata for solar PV system," Energy, Elsevier, vol. 112(C), pages 1096-1106.
    8. Gang Zhang & Zhongbei Tian & Huiqing Du & Zhigang Liu, 2018. "A Novel Hybrid DC Traction Power Supply System Integrating PV and Reversible Converters," Energies, MDPI, vol. 11(7), pages 1-24, June.
    9. Farhat, Maissa & Barambones, Oscar & Sbita, Lassaad, 2017. "A new maximum power point method based on a sliding mode approach for solar energy harvesting," Applied Energy, Elsevier, vol. 185(P2), pages 1185-1198.
    10. Ashish Kumar Singhal & Narendra Singh Beniwal & Ruby Beniwal & Krzysztof Lalik, 2022. "An Experimental Study of Drift Caused by Partial Shading Using a Modified DC-(P&O) Technique for a Stand-Alone PV System," Energies, MDPI, vol. 15(12), pages 1-21, June.
    11. Messalti, Sabir & Harrag, Abdelghani & Loukriz, Abdelhamid, 2017. "A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 221-233.
    12. Maisonneuve, Jonathan & Chintalacheruvu, Sanjana, 2019. "Increasing osmotic power and energy with maximum power point tracking," Applied Energy, Elsevier, vol. 238(C), pages 683-695.
    13. Ankit, & Sahoo, Sarat Kumar & Sukchai, Sukruedee & Yanine, Franco Fernando, 2018. "Review and comparative study of single-stage inverters for a PV system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 962-986.
    14. Nunes, Pedro & Figueiredo, Raquel & Brito, Miguel C., 2016. "The use of parking lots to solar-charge electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 679-693.
    15. 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.
    16. Filip Žemla & Ján Cigánek & Danica Rosinová & Erik Kučera & Oto Haffner, 2023. "Complex Positioning System for the Control and Visualization of Photovoltaic Systems," Energies, MDPI, vol. 16(10), pages 1-31, May.
    17. Chanuri Charin & Dahaman Ishak & Muhammad Ammirrul Atiqi Mohd Zainuri & Baharuddin Ismail & Turki Alsuwian & Adam R. H. Alhawari, 2022. "Modified Levy-based Particle Swarm Optimization (MLPSO) with Boost Converter for Local and Global Point Tracking," Energies, MDPI, vol. 15(19), pages 1-30, October.
    18. Memon, Mudasir Ahmed & Mekhilef, Saad & Mubin, Marizan & Aamir, Muhammad, 2018. "Selective harmonic elimination in inverters using bio-inspired intelligent algorithms for renewable energy conversion applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2235-2253.
    19. Hao, Daning & Qi, Lingfei & Tairab, Alaeldin M. & Ahmed, Ammar & Azam, Ali & Luo, Dabing & Pan, Yajia & Zhang, Zutao & Yan, Jinyue, 2022. "Solar energy harvesting technologies for PV self-powered applications: A comprehensive review," Renewable Energy, Elsevier, vol. 188(C), pages 678-697.
    20. Kofinas, P. & Doltsinis, S. & Dounis, A.I. & Vouros, G.A., 2017. "A reinforcement learning approach for MPPT control method of photovoltaic sources," Renewable Energy, Elsevier, vol. 108(C), pages 461-473.

    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:13:p:7208-:d:583503. 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.