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An Adaptive Model-Based MPPT Technique with Drift-Avoidance for Grid-Connected PV Systems

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  • Mostafa Ahmed

    (Institute for Electrical Drive Systems and Power Electronics, Technical University of Munich (TUM), 80333 Munich, Germany
    Electrical Engineering Department, Faculty of Engineering, Assiut University, Assiut 71516, Egypt)

  • Mohamed Abdelrahem

    (Institute for Electrical Drive Systems and Power Electronics, Technical University of Munich (TUM), 80333 Munich, Germany
    Electrical Engineering Department, Faculty of Engineering, Assiut University, Assiut 71516, Egypt)

  • Ibrahim Harbi

    (Institute for Electrical Drive Systems and Power Electronics, Technical University of Munich (TUM), 80333 Munich, Germany
    Electrical Engineering Department, Faculty of Engineering, Menoufia University, Shebin El-Koum 32511, Egypt)

  • Ralph Kennel

    (Institute for Electrical Drive Systems and Power Electronics, Technical University of Munich (TUM), 80333 Munich, Germany)

Abstract

In this article, a modified control structure for a single-stage three phase grid-connected photovoltaic (PV) system is presented. In the proposed system, the maximum power point tracking (MPPT) function is developed using a new adaptive model-based technique, in which the maximum power point (MPP) voltage can be precisely located based on the characteristics of the PV source. By doing so, the drift problem associated with the traditional perturb and observe (P&O) technique can be easily solved. Moreover, the inverter control is accomplished using a predictive dead-beat function, which directly estimates the required reference voltages from the commanded reference currents. Then, the reference voltages are applied to a space vector pulse width modulator (SVPWM) for switching state generation. Furthermore, the proposed inverter control avoids the conventional and known cascaded loop structure of the voltage oriented control (VOC) method by elimination of the outer PI controller, and hence the overall control strategy is simplified. The proposed system is compared with different MPPT techniques, including the conventional P&O method and other techniques intended for drift avoidance. The evaluation of the suggested control methodology depends on various radiation profiles created in MATLAB. The proposed technique succeeds at capturing the maximum available power from the PV source with no drift in comparison with other methods.

Suggested Citation

  • Mostafa Ahmed & Mohamed Abdelrahem & Ibrahim Harbi & Ralph Kennel, 2020. "An Adaptive Model-Based MPPT Technique with Drift-Avoidance for Grid-Connected PV Systems," Energies, MDPI, vol. 13(24), pages 1-25, December.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6656-:d:463598
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    References listed on IDEAS

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    1. Hassaine, L. & OLias, E. & Quintero, J. & Salas, V., 2014. "Overview of power inverter topologies and control structures for grid connected photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 796-807.
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    3. Ibrahim Harbi & Mohamed Abdelrahem & Mostafa Ahmed & Ralph Kennel, 2020. "Reduced-Complexity Model Predictive Control with Online Parameter Assessment for a Grid-Connected Single-Phase Multilevel Inverter," Sustainability, MDPI, vol. 12(19), pages 1-23, September.
    4. Ishaque, Kashif & Salam, Zainal & Lauss, George, 2014. "The performance of perturb and observe and incremental conductance maximum power point tracking method under dynamic weather conditions," Applied Energy, Elsevier, vol. 119(C), pages 228-236.
    5. Hosseini, Seyed Ehsan & Wahid, Mazlan Abdul, 2016. "Hydrogen production from renewable and sustainable energy resources: Promising green energy carrier for clean development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 850-866.
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    7. Verma, Deepak & Nema, Savita & Shandilya, A.M. & Dash, Soubhagya K., 2016. "Maximum power point tracking (MPPT) techniques: Recapitulation in solar photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1018-1034.
    8. Zahedi, A., 2011. "Maximizing solar PV energy penetration using energy storage technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 866-870, January.
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    10. Mostafa Ahmed & Mohamed Abdelrahem & Ralph Kennel, 2020. "Highly Efficient and Robust Grid Connected Photovoltaic System Based Model Predictive Control with Kalman Filtering Capability," Sustainability, MDPI, vol. 12(11), pages 1-22, June.
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    Cited by:

    1. Naoui Mohamed & Flah Aymen & Abdullah Altamimi & Zafar A. Khan & Sbita Lassaad, 2022. "Power Management and Control of a Hybrid Electric Vehicle Based on Photovoltaic, Fuel Cells, and Battery Energy Sources," Sustainability, MDPI, vol. 14(5), pages 1-20, February.
    2. Ashish Kumar Singhal & Narendra Singh Beniwal & Ruby Beniwal & Krzysztof Lalik, 2022. "An Experimental Study of Drift Caused by Partial Shading Using a Modified DC-(P&O) Technique for a Stand-Alone PV System," Energies, MDPI, vol. 15(12), pages 1-21, June.
    3. Mostafa Ahmed & Ibrahim Harbi & Ralph Kennel & José Rodríguez & Mohamed Abdelrahem, 2022. "Evaluation of the Main Control Strategies for Grid-Connected PV Systems," Sustainability, MDPI, vol. 14(18), pages 1-20, September.
    4. Sachin Angadi & Udaykumar R. Yaragatti & Yellasiri Suresh & A. B. Raju, 2021. "System Parameter Based Performance Optimization of Solar PV Systems with Perturbation Based MPPT Algorithms," Energies, MDPI, vol. 14(7), pages 1-20, April.

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