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Maximum Power Point Tracking of PV System Based on Machine Learning

Author

Listed:
  • Maen Takruri

    (Department of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Ras Al Khamah, UAE)

  • Maissa Farhat

    (Department of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Ras Al Khamah, UAE)

  • Oscar Barambones

    (Systems Engineering and Automatic Control Department, Faculty of Engineering Vitoria-Gasteiz, University of the Basque Country, 01006 Vitoria-Gasteiz, Spain)

  • José Antonio Ramos-Hernanz

    (Electrical Engineering Department, Faculty of Engineering Vitoria-Gasteiz, University of the Basque Country, 01006 Vitoria-Gasteiz, Spain)

  • Mohammed Jawdat Turkieh

    (Department of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Ras Al Khamah, UAE)

  • Mohammed Badawi

    (Department of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Ras Al Khamah, UAE)

  • Hanin AlZoubi

    (Department of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Ras Al Khamah, UAE)

  • Maswood Abdus Sakur

    (Department of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Ras Al Khamah, UAE)

Abstract

This project studies the conditions at which the maximum power point of a photovoltaic (PV) panel is obtained. It shows that the maximum power point is very sensitive to external disturbances such as temperature and irradiation. It introduces a novel method for maximizing the output power of a PV panel when connected to a DC/DC boost converter under variable load conditions. The main contribution of this work is to predict the optimum reference voltage of the PV panel at all-weather conditions using machine learning strategies and to use it as a reference for a Proportional-Integral-Derivative controller that ensures that the DC/DC boost converter provides a stable output voltage and maximum power under different weather conditions and loads. Evaluations of the proposed system, which uses an experimental photovoltaic dataset gathered from Spain, prove that it is robust against internal and external disturbances. They also show that the system performs better when using support vector machines as the machine learning strategy compared to the case when using general regression neural networks.

Suggested Citation

  • Maen Takruri & Maissa Farhat & Oscar Barambones & José Antonio Ramos-Hernanz & Mohammed Jawdat Turkieh & Mohammed Badawi & Hanin AlZoubi & Maswood Abdus Sakur, 2020. "Maximum Power Point Tracking of PV System Based on Machine Learning," Energies, MDPI, vol. 13(3), pages 1-14, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:692-:d:316911
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    References listed on IDEAS

    as
    1. Noureddine Bouarroudj & Djamel Boukhetala & Vicente Feliu-Batlle & Fares Boudjema & Boualam Benlahbib & Bachir Batoun, 2019. "Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System," Energies, MDPI, vol. 12(14), pages 1-19, July.
    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. Qunli Wu & Chenyang Peng, 2016. "Wind Power Generation Forecasting Using Least Squares Support Vector Machine Combined with Ensemble Empirical Mode Decomposition, Principal Component Analysis and a Bat Algorithm," Energies, MDPI, vol. 9(4), pages 1-19, April.
    4. Chen, Ji-Long & Liu, Hong-Bin & Wu, Wei & Xie, De-Ti, 2011. "Estimation of monthly solar radiation from measured temperatures using support vector machines – A case study," Renewable Energy, Elsevier, vol. 36(1), pages 413-420.
    5. Luigi Piegari & Renato Rizzo & Ivan Spina & Pietro Tricoli, 2015. "Optimized Adaptive Perturb and Observe Maximum Power Point Tracking Control for Photovoltaic Generation," Energies, MDPI, vol. 8(5), pages 1-19, April.
    6. Farhat, Maissa & Barambones, Oscar & Sbita, Lassaâd, 2015. "Efficiency optimization of a DSP-based standalone PV system using a stable single input fuzzy logic controller," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 907-920.
    7. Syed Zulqadar Hassan & Hui Li & Tariq Kamal & Uğur Arifoğlu & Sidra Mumtaz & Laiq Khan, 2017. "Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 10(3), pages 1-16, March.
    8. Farhat, Maissa & Barambones, Oscar & Sbita, Lassaad, 2017. "A new maximum power point method based on a sliding mode approach for solar energy harvesting," Applied Energy, Elsevier, vol. 185(P2), pages 1185-1198.
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    Cited by:

    1. Faiçal Hamidi & Severus Constantin Olteanu & Dumitru Popescu & Houssem Jerbi & Ingrid Dincă & Sondess Ben Aoun & Rabeh Abbassi, 2020. "Model Based Optimisation Algorithm for Maximum Power Point Tracking in Photovoltaic Panels," Energies, MDPI, vol. 13(18), pages 1-20, September.
    2. Ahmad Manasrah & Mohammad Masoud & Yousef Jaradat & Piero Bevilacqua, 2022. "Investigation of a Real-Time Dynamic Model for a PV Cooling System," Energies, MDPI, vol. 15(5), pages 1-15, March.
    3. Fabio Corti & Antonino Laudani & Gabriele Maria Lozito & Alberto Reatti, 2020. "Computationally Efficient Modeling of DC-DC Converters for PV Applications," Energies, MDPI, vol. 13(19), pages 1-18, September.
    4. Zulfiqar Ali & Syed Zagam Abbas & Anzar Mahmood & Syed Wajahat Ali & Syed Bilal Javed & Chun-Lien Su, 2023. "A Study of a Generalized Photovoltaic System with MPPT Using Perturb and Observer Algorithms under Varying Conditions," Energies, MDPI, vol. 16(9), pages 1-21, April.

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