IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i16p12402-d1217712.html
   My bibliography  Save this article

A Hybrid Particle Swarm Optimization with Butterfly Optimization Algorithm Based Maximum Power Point Tracking for Photovoltaic Array under Partial Shading Conditions

Author

Listed:
  • Yonggang Wang

    (College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Shengnan Dai

    (College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Pinchi Liu

    (College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, China
    Substation Operation and Inspection Office, State Grid Fushun Power Supply Company, Fushun 113008, China)

  • Xinyu Zhao

    (College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, China)

Abstract

The key objective of this paper is to develop a photovoltaic (PV) maximum power point tracking (MPPT) algorithm based on particle swarm optimization–butterfly optimization algorithm (PSO-BOA) that is adapted for partial shading conditions (PSCs). Generally, conventional MPPT techniques are often unable to accurately locate the global maximum power point (GMPP) generated by partial shading in PV systems. As a result, a significant decrease in power output occurs. The traditional particle swarm optimization (PSO) algorithm traps the local maxima point easily, while the butterfly optimization algorithm (BOA) has slow convergence speed and large oscillations during its use in research. To address the limitations of the aforementioned PSO and BOA algorithms, the MPPT strategy of PV systems combining PSO-BOA is presented, which can ameliorate the efficiency and accuracy in PSCs. In this paper, the control parameter of sensory modality in the BOA can be acquired based on logistic mapping, and the self-adaptive adjustment of the inertial weight of the PSO algorithm is designed. According to the simulation findings, the suggested method is more suitable than PSO and BOA with respect to intricate shading-induced variations in irradiance and changes in external temperatures. The average tracking time is less than 0.5 s, and the tracking accuracy is not less than 99.94%. Especially under sudden variations in irradiance and temperature conditions, the tracking time of the PSO-BOA algorithm is only 49.70% of that of the PSO algorithm and 55.63% of that of the BOA. Therefore, the MPPT method presented has the ability to improve the oscillations and result in less convergence speed, which in turn accurately tracks the GMPP.

Suggested Citation

  • Yonggang Wang & Shengnan Dai & Pinchi Liu & Xinyu Zhao, 2023. "A Hybrid Particle Swarm Optimization with Butterfly Optimization Algorithm Based Maximum Power Point Tracking for Photovoltaic Array under Partial Shading Conditions," Sustainability, MDPI, vol. 15(16), pages 1-21, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12402-:d:1217712
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/16/12402/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/16/12402/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rodrigo Bairros Watanabe & Oswaldo Hideo Ando Junior & Paulo Gabriel Martins Leandro & Fabiano Salvadori & Marlon Felipe Beck & Katiane Pereira & Marcelo Henrique Manzque Brandt & Fernando Marcos de O, 2022. "Implementation of the Bio-Inspired Metaheuristic Firefly Algorithm (FA) Applied to Maximum Power Point Tracking of Photovoltaic Systems," Energies, MDPI, vol. 15(15), pages 1-15, July.
    2. 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.
    3. Kuei-Hsiang Chao & Muhammad Nursyam Rizal, 2021. "A Hybrid MPPT Controller Based on the Genetic Algorithm and Ant Colony Optimization for Photovoltaic Systems under Partially Shaded Conditions," Energies, MDPI, vol. 14(10), pages 1-17, May.
    4. Fatima Z. Khemili & Omar Bouhali & Moussa Lefouili & Lakhdar Chaib & Attia A. El-Fergany & Ahmed M. Agwa, 2023. "Design of Cascaded Multilevel Inverter and Enhanced MPPT Method for Large-Scale Photovoltaic System Integration," Sustainability, MDPI, vol. 15(12), pages 1-20, June.
    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. Wang, Yonggang & Zhao, Kaixing & Hao, Yue & Yao, Yilin, 2024. "Short-term wind power prediction using a novel model based on butterfly optimization algorithm-variational mode decomposition-long short-term memory," Applied Energy, Elsevier, vol. 366(C).
    2. Yonggang Wang & Fuxian Li & Ruimin Xiao & Nannan Zhang, 2024. "A Systematic Investigation into the Optimization of Reactive Power in Distribution Networks Using the Improved Sparrow Search Algorithm–Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 17(9), pages 1-22, April.

    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. Yao, Ganzhou & Luo, Zirong & Lu, Zhongyue & Wang, Mangkuan & Shang, Jianzhong & Guerrerob, Josep M., 2023. "Unlocking the potential of wave energy conversion: A comprehensive evaluation of advanced maximum power point tracking techniques and hybrid strategies for sustainable energy harvesting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    2. Camilo, Jones C. & Guedes, Tatiana & Fernandes, Darlan A. & Melo, J.D. & Costa, F.F. & Sguarezi Filho, Alfeu J., 2019. "A maximum power point tracking for photovoltaic systems based on Monod equation," Renewable Energy, Elsevier, vol. 130(C), pages 428-438.
    3. Hassan M. H. Farh & Mohd F. Othman & Ali M. Eltamaly & M. S. Al-Saud, 2018. "Maximum Power Extraction from a Partially Shaded PV System Using an Interleaved Boost Converter," Energies, MDPI, vol. 11(10), pages 1-18, September.
    4. Weng-Hooi Tan & Junita Mohamad-Saleh, 2023. "Critical Review on Interrelationship of Electro-Devices in PV Solar Systems with Their Evolution and Future Prospects for MPPT Applications," Energies, MDPI, vol. 16(2), pages 1-37, January.
    5. Abderrazek Saoudi & Saber Krim & Mohamed Faouzi Mimouni, 2021. "Enhanced Intelligent Closed Loop Direct Torque and Flux Control of Induction Motor for Standalone Photovoltaic Water Pumping System," Energies, MDPI, vol. 14(24), pages 1-21, December.
    6. Kwan, Trevor Hocksun & Wu, Xiaofeng, 2017. "The Lock-On Mechanism MPPT algorithm as applied to the hybrid photovoltaic cell and thermoelectric generator system," Applied Energy, Elsevier, vol. 204(C), pages 873-886.
    7. Mostafa Ahmed & Mohamed Abdelrahem & Ibrahim Harbi & Ralph Kennel, 2020. "An Adaptive Model-Based MPPT Technique with Drift-Avoidance for Grid-Connected PV Systems," Energies, MDPI, vol. 13(24), pages 1-25, December.
    8. Guo, Lei & Meng, Zhuo & Sun, Yize & Wang, Libiao, 2018. "A modified cat swarm optimization based maximum power point tracking method for photovoltaic system under partially shaded condition," Energy, Elsevier, vol. 144(C), pages 501-514.
    9. Abbes Kihal & Fateh Krim & Billel Talbi & Abdelbaset Laib & Abdeslem Sahli, 2018. "A Robust Control of Two-Stage Grid-Tied PV Systems Employing Integral Sliding Mode Theory," Energies, MDPI, vol. 11(10), pages 1-21, October.
    10. Chatterjee, Shantanu & Kumar, Prashant & Chatterjee, Saibal, 2018. "A techno-commercial review on grid connected photovoltaic system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2371-2397.
    11. Başoğlu, Mustafa Engin & Çakır, Bekir, 2016. "Comparisons of MPPT performances of isolated and non-isolated DC–DC converters by using a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1100-1113.
    12. Kebir, Anouer & Woodward, Lyne & Akhrif, Ouassima, 2019. "Real-time optimization of renewable energy sources power using neural network-based anticipative extremum-seeking control," Renewable Energy, Elsevier, vol. 134(C), pages 914-926.
    13. Elkin Edilberto Henao-Bravo & Carlos Andrés Ramos-Paja & Andrés Julián Saavedra-Montes & Daniel González-Montoya & Julián Sierra-Pérez, 2020. "Design Method of Dual Active Bridge Converters for Photovoltaic Systems with High Voltage Gain," Energies, MDPI, vol. 13(7), pages 1-31, April.
    14. Vavilapalli, Sridhar & Umashankar, S. & Sanjeevikumar, P. & Ramachandaramurthy, Vigna K. & Mihet-Popa, Lucian & Fedák, Viliam, 2018. "Three-stage control architecture for cascaded H-Bridge inverters in large-scale PV systems – Real time simulation validation," Applied Energy, Elsevier, vol. 229(C), pages 1111-1127.
    15. Arshdeep Singh & Shimi Sudha Letha, 2019. "Emerging energy sources for electric vehicle charging station," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(5), pages 2043-2082, October.
    16. Fathabadi, Hassan, 2016. "Novel fast dynamic MPPT (maximum power point tracking) technique with the capability of very high accurate power tracking," Energy, Elsevier, vol. 94(C), pages 466-475.
    17. 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.
    18. Li, Shaowu, 2016. "Linear equivalent models at the maximum power point based on variable weather parameters for photovoltaic cell," Applied Energy, Elsevier, vol. 182(C), pages 94-104.
    19. Belkaid, A. & Colak, I. & Isik, O., 2016. "Photovoltaic maximum power point tracking under fast varying of solar radiation," Applied Energy, Elsevier, vol. 179(C), pages 523-530.
    20. Ahmad Dawahdeh & Hussein Sharadga & Sunil Kumar, 2024. "Novel MPPT Controller Augmented with Neural Network for Use with Photovoltaic Systems Experiencing Rapid Solar Radiation Changes," Sustainability, MDPI, vol. 16(3), pages 1-22, January.

    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:15:y:2023:i:16:p:12402-:d:1217712. 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.