IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v283y2023ics036054422302563x.html
   My bibliography  Save this article

Archimedes optimization algorithm (AOA)-Based global maximum power point tracking for a photovoltaic system under partial and complex shading conditions

Author

Listed:
  • Sajid, Injila
  • Sarwar, Adil
  • Tariq, Mohd
  • Bakhsh, Farhad Ilahi
  • Ahmad, Shafiq
  • Shah Noor Mohamed, Adamali

Abstract

During partial shading conditions (PSCs), the efficiency of power transfer in a Photovoltaic (PV) system decreases significantly, which can result in the formation of hotspots in the PV array. Although the insertion of bypass diodes can alleviate this issue, it can result in multiple power peaks on the power-voltage (P–V) characteristics and complicate the process of maximum power tracking. To tackle this problem, using metaheuristic algorithms for Maximum Power Point Tracking (MPPT) can yield favourable results by avoiding convergence to local power peaks and reducing computation stress on the microcontroller. However, continuous research is required in this area due to variations in the performance of metaheuristic algorithms. Therefore, this work introduces a novel MPPT approach based on the Archimedes Optimization Algorithm (AOA) for the successful capture of the Maximum Power Point (MPP) under various PS scenarios. The performance of AOA is evaluated against the other state-of-the-art algorithms such as particle swarm optimization (PSO), Jaya, and the newly proposed variant of Jaya called the adaptive Jaya (A-Jaya). The proposed MPPT method is analysed in MATLAB/Simulink software and validated using real-time results obtained from Typhoon Hardware-in-the-loop (HIL)-402 emulator. The proposed algorithm outperforms the previous algorithms, as evidenced by a comparison of the results in terms of power tracking efficiency, tracking time, and the quantity of power fluctuations.

Suggested Citation

  • Sajid, Injila & Sarwar, Adil & Tariq, Mohd & Bakhsh, Farhad Ilahi & Ahmad, Shafiq & Shah Noor Mohamed, Adamali, 2023. "Archimedes optimization algorithm (AOA)-Based global maximum power point tracking for a photovoltaic system under partial and complex shading conditions," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s036054422302563x
    DOI: 10.1016/j.energy.2023.129169
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422302563X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129169?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Abdulaziz Almutairi & Ahmed G. Abo-Khalil & Khairy Sayed & Naif Albagami, 2020. "MPPT for a PV Grid-Connected System to Improve Efficiency under Partial Shading Conditions," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    3. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    4. Ahmed G. Abo-Khalil & Walied Alharbi & Abdel-Rahman Al-Qawasmi & Mohammad Alobaid & Ibrahim M. Alarifi, 2021. "Maximum Power Point Tracking of PV Systems under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    5. Ghazi A. Ghazi & Hany M. Hasanien & Essam A. Al-Ammar & Rania A. Turky & Wonsuk Ko & Sisam Park & Hyeong-Jin Choi, 2022. "African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems," Sustainability, MDPI, vol. 14(13), pages 1-26, July.
    6. Yu-Pei Huang & Cheng-En Ye & Xiang Chen, 2018. "A Modified Firefly Algorithm with Rapid Response Maximum Power Point Tracking for Photovoltaic Systems under Partial Shading Conditions," Energies, MDPI, vol. 11(9), pages 1-33, August.
    7. Diogo Cabral & Abolfazl Hayati & João Gomes & Hossein Afzali Gorouh & Pouriya Nasseriyan & Mazyar Salmanzadeh, 2023. "Experimental Electrical Assessment Evaluation of a Vertical n-PERT Half-Size Bifacial Solar Cell String Receiver on a Parabolic Trough Solar Collector," Energies, MDPI, vol. 16(4), pages 1-21, February.
    8. Ali M. Eltamaly, 2021. "A Novel Strategy for Optimal PSO Control Parameters Determination for PV Energy Systems," Sustainability, MDPI, vol. 13(2), pages 1-28, January.
    9. Ali Abedaljabar Al-Samawi & Hafedh Trabelsi, 2022. "New Nine-Level Cascade Multilevel Inverter with a Minimum Number of Switches for PV Systems," Energies, MDPI, vol. 15(16), pages 1-25, August.
    10. Lu, Zhen & Huang, Yuewu & Zhao, Yonggang, 2023. "Elastocaloric cooler for waste heat recovery from perovskite solar cell with electricity and cooling production," Renewable Energy, Elsevier, vol. 215(C).
    11. Eyal Amer & Alon Kuperman & Teuvo Suntio, 2019. "Direct Fixed-Step Maximum Power Point Tracking Algorithms with Adaptive Perturbation Frequency," Energies, MDPI, vol. 12(3), pages 1-16, January.
    12. 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.
    13. Majed A. Alotaibi & Ali M. Eltamaly, 2021. "A Smart Strategy for Sizing of Hybrid Renewable Energy System to Supply Remote Loads in Saudi Arabia," Energies, MDPI, vol. 14(21), pages 1-24, October.
    14. Tamir Shaqarin, 2023. "Particle Swarm Optimization with Targeted Position-Mutated Elitism (PSO-TPME) for Partially Shaded PV Systems," Sustainability, MDPI, vol. 15(5), pages 1-23, February.
    15. Ali Bughneda & Mohamed Salem & Anna Richelli & Dahaman Ishak & Salah Alatai, 2021. "Review of Multilevel Inverters for PV Energy System Applications," Energies, MDPI, vol. 14(6), pages 1-23, March.
    16. Sy Ngo & Chian-Song Chiu & Thanh-Dong Ngo, 2022. "A Novel Horse Racing Algorithm Based MPPT Control for Standalone PV Power Systems," Energies, MDPI, vol. 15(20), pages 1-18, October.
    17. Jately, Vibhu & Azzopardi, Brian & Joshi, Jyoti & Venkateswaran V, Balaji & Sharma, Abhinav & Arora, Sudha, 2021. "Experimental Analysis of hill-climbing MPPT algorithms under low irradiance levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    18. 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.
    19. 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.
    20. Gao, Fang & Hu, Rongzhao & Yin, Linfei, 2023. "Variable boundary reinforcement learning for maximum power point tracking of photovoltaic grid-connected systems," Energy, Elsevier, vol. 264(C).

    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:eee:energy:v:283:y:2023:i:c:s036054422302563x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.