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Novel MPPT Controller Augmented with Neural Network for Use with Photovoltaic Systems Experiencing Rapid Solar Radiation Changes

Author

Listed:
  • Ahmad Dawahdeh

    (Department of Mechanical Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan
    These authors contributed equally to this work.)

  • Hussein Sharadga

    (Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA
    These authors contributed equally to this work.)

  • Sunil Kumar

    (Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA)

Abstract

A maximum power point tracking (MPPT) controller optimizes power harvesting in photovoltaic (PV) systems under varying conditions. The perturb and observation (P&O) algorithm is commonly used for MPP tracking, but suffers from slow response, loss of tracking direction, and entrapment. The current research proposes a neural network (NN) integrated with the P&O algorithm to enhance tracking performance during sudden variations in solar irradiance. The proposed neural network updates the duty cycle change when detecting sudden changes. It effectively estimates the duty cycle change even when trained with a small dataset. The integration between the NN and P&O significantly improves tracking performance compared with the conventional P&O algorithm, especially under sudden irradiance changes.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:1021-:d:1325976
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    References listed on IDEAS

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    1. 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.
    2. Rezk, Hegazy & Fathy, Ahmed & Abdelaziz, Almoataz Y., 2017. "A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 377-386.
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