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Grid-connected modular PV-Converter system with shuffled frog leaping algorithm based DMPPT controller

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  • Mao, Mingxuan
  • Zhang, Li
  • Duan, Pan
  • Duan, Qichang
  • Yang, Ming

Abstract

Maximum power extraction for PV systems with multiple panels under partial shading conditions (PSCs) relies on the configuration of the system and the optimal searching algorithms used. This paper described a PV system with multiple PV panels in series. Each panel has a dc-dc step-down converter, hence allowing independent control of load and source power ratio corresponding to the irradiation levels. An H-bridge terminal inverter is also used for grid connection. An advanced searching algorithm (TSPSOEM) is proposed in the paper for the distributed maximum power point tracking (DMPPT). This applies the basic particle swarm optimization (PSO) procedure but with an extended memory and incorporating the grouping concept from shuffled frog leaping algorithm (SFLA). The new algorithm is applied simultaneously to all PV-converter modules in the chain. The system can exploit the variable converter ratios and reduces the effect of differential shading, both between panels and across panels. The paper presents the system and the proposed new algorithm and demonstrating superior results obtained when compared with other conventional methods.

Suggested Citation

  • Mao, Mingxuan & Zhang, Li & Duan, Pan & Duan, Qichang & Yang, Ming, 2018. "Grid-connected modular PV-Converter system with shuffled frog leaping algorithm based DMPPT controller," Energy, Elsevier, vol. 143(C), pages 181-190.
    • Handle: RePEc:eee:energy:v:143:y:2018:i:c:p:181-190
    DOI: 10.1016/j.energy.2017.10.099
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Fathabadi, Hassan, 2016. "Novel highly accurate universal maximum power point tracker for maximum power extraction from hybrid fuel cell/photovoltaic/wind power generation systems," Energy, Elsevier, vol. 116(P1), pages 402-416.
    4. Sudha Letha, Shimi & Thakur, Tilak & Kumar, Jagdish, 2016. "Harmonic elimination of a photo-voltaic based cascaded H-bridge multilevel inverter using PSO (particle swarm optimization) for induction motor drive," Energy, Elsevier, vol. 107(C), pages 335-346.
    5. Boukenoui, R. & Ghanes, M. & Barbot, J.-P. & Bradai, R. & Mellit, A. & Salhi, H., 2017. "Experimental assessment of Maximum Power Point Tracking methods for photovoltaic systems," Energy, Elsevier, vol. 132(C), pages 324-340.
    6. 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.
    7. 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.
    8. 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.
    9. Chin, Vun Jack & Salam, Zainal & Ishaque, Kashif, 2015. "Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review," Applied Energy, Elsevier, vol. 154(C), pages 500-519.
    10. 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.
    11. Hasan, Rasedul & Mekhilef, Saad & Seyedmahmoudian, Mehdi & Horan, Ben, 2017. "Grid-connected isolated PV microinverters: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 1065-1080.
    12. Abdalla, I. & Corda, J. & Zhang, L., 2016. "Optimal control of a multilevel DC-link converter photovoltaic system for maximum power generation," Renewable Energy, Elsevier, vol. 92(C), pages 1-11.
    13. Rajesh, R. & Mabel, M. Carolin, 2016. "Design and real time implementation of a novel rule compressed fuzzy logic method for the determination operating point in a photo voltaic system," Energy, Elsevier, vol. 116(P1), pages 140-153.
    14. Kannan, Nadarajah & Vakeesan, Divagar, 2016. "Solar energy for future world: - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 1092-1105.
    15. Hong, Chih-Ming & Ou, Ting-Chia & Lu, Kai-Hung, 2013. "Development of intelligent MPPT (maximum power point tracking) control for a grid-connected hybrid power generation system," Energy, Elsevier, vol. 50(C), pages 270-279.
    16. Prasanth Ram, J. & Rajasekar, N., 2017. "A new global maximum power point tracking technique for solar photovoltaic (PV) system under partial shading conditions (PSC)," Energy, Elsevier, vol. 118(C), pages 512-525.
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    2. de Oliveira-Assis, Lais & Soares-Ramos, Emanuel P.P. & Sarrias-Mena, Raúl & García-Triviño, Pablo & González-Rivera, Enrique & Sánchez-Sainz, Higinio & Llorens-Iborra, Francisco & Fernández-Ramírez, L, 2022. "Simplified model of battery energy-stored quasi-Z-source inverter-based photovoltaic power plant with Twofold energy management system," Energy, Elsevier, vol. 244(PA).
    3. Ezhilmaran Ranganathan & Rajasekar Natarajan, 2022. "Spotted Hyena Optimization Method for Harvesting Maximum PV Power under Uniform and Partial-Shade Conditions," Energies, MDPI, vol. 15(8), pages 1-26, April.
    4. Yin, Linfei & Gao, Qi & Zhao, Lulin & Wang, Tao, 2020. "Expandable deep learning for real-time economic generation dispatch and control of three-state energies based future smart grids," Energy, Elsevier, vol. 191(C).

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