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Intelligent Reconfigurable Photovoltaic System

Author

Listed:
  • Ekaterina Engel

    (Information Technologies & Systems Department, Katanov State University of Khakassia, 655017 Abakan, Russia)

  • Igor Kovalev

    (Department of Computing and Information Technology, Siberian Federal University, 660000 Krasnoyarsk, Russia)

  • Nikolay Testoyedov

    (Academician M.F. Reshetnev Information Satellite Systems, 660000 Krasnoyarsk, Russia)

  • Nikita E. Engel

    (Information Technologies & Systems Department, Katanov State University of Khakassia, 655017 Abakan, Russia)

Abstract

The global maximum power point tracking of a PV array under partial shading represents a global optimization problem. Conventional maximum power point tracking algorithms fail to track the global maximum power point, and global optimization algorithms do not provide global maximum power point in real-time mode due to a slow convergence process. This paper presents an intelligent reconfigurable photovoltaic system on the basis of a modified fuzzy neural net that includes a convolutional block, recurrent networks, and fuzzy units. We tune the modified fuzzy neural net based on modified multi-dimension particle swarm optimization. Based on the processing of the sensors’ signals and the photovoltaic array’s image, the tuned modified fuzzy neural net generates an electrical interconnection matrix of a photovoltaic total-cross-tied array, which reaches the global maximum power point under non-homogeneous insolation. Thus, the intelligent reconfigurable photovoltaic system represents an effective machine learning application in a photovoltaic system. We demonstrate the advantages of the created intelligent reconfigurable photovoltaic system by simulations. The simulation results reveal robustness against photovoltaic system uncertainties and better performance and control speed of the proposed intelligent reconfigurable photovoltaic system under non-homogeneous insolation as compared to a GA-based reconfiguration total-cross-tied photovoltaic system.

Suggested Citation

  • Ekaterina Engel & Igor Kovalev & Nikolay Testoyedov & Nikita E. Engel, 2021. "Intelligent Reconfigurable Photovoltaic System," Energies, MDPI, vol. 14(23), pages 1-11, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7969-:d:690791
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    References listed on IDEAS

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    1. Deshkar, Shubhankar Niranjan & Dhale, Sumedh Bhaskar & Mukherjee, Jishnu Shekar & Babu, T. Sudhakar & Rajasekar, N., 2015. "Solar PV array reconfiguration under partial shading conditions for maximum power extraction using genetic algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 102-110.
    2. Gautam, Nalin K. & Kaushika, N.D., 2002. "An efficient algorithm to simulate the electrical performance of solar photovoltaic arrays," Energy, Elsevier, vol. 27(4), pages 347-361.
    3. Ramli, Makbul A.M. & Twaha, Ssennoga & Ishaque, Kashif & Al-Turki, Yusuf A., 2017. "A review on maximum power point tracking for photovoltaic systems with and without shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 144-159.
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    1. Tiago H. de A. Mateus & José A. Pomilio & Ruben B. Godoy & João O. P. Pinto, 2022. "VSG Control Applied to Seven-Level PV Inverter for Partial Shading Impact Abatement," Energies, MDPI, vol. 15(17), pages 1-14, September.

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