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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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    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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