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Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems

Author

Listed:
  • Suliang Ma

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
    These authors contributed equally to this work.)

  • Mingxuan Chen

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
    These authors contributed equally to this work.)

  • Jianwen Wu

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
    These authors contributed equally to this work.)

  • Wenlei Huo

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
    These authors contributed equally to this work.)

  • Lian Huang

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
    These authors contributed equally to this work.)

Abstract

Photovoltaic (PV) systems have non-linear characteristics that generate maximum power at one particular operating point. Environmental factors such as irradiance and temperature variations greatly affect the maximum power point (MPP). Diverse offline and online techniques have been introduced for tracking the MPP. Here, to track the MPP, an augmented-state feedback linearized (AFL) non-linear controller combined with an artificial neural network (ANN) is proposed. This approach linearizes the non-linear characteristics in PV systems and DC/DC converters, for tracking and optimizing the PV system operation. It also reduces the dependency of the designed controller on linearized models, to provide global stability. A complete model of the PV system is simulated. The existing maximum power-point tracking (MPPT) and DC/DC boost-converter controller techniques are compared with the proposed ANN method. Two case studies, which simulate realistic circumstances, are presented to demonstrate the effectiveness and superiority of the proposed method. The AFL with ANN controller can provide good dynamic operation, faster convergence speed, and fewer operating-point oscillations around the MPP. It also tracks the global maxima under different conditions, especially irradiance-mutating situations, more effectively than the conventional methods. Detailed mathematical models and a control approach for a three-phase grid-connected intelligent hybrid system are proposed using MATLAB/Simulink.

Suggested Citation

  • Suliang Ma & Mingxuan Chen & Jianwen Wu & Wenlei Huo & Lian Huang, 2016. "Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems," Energies, MDPI, vol. 9(12), pages 1-24, November.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:1005-:d:84031
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    References listed on IDEAS

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    Cited by:

    1. Diego R. Espinoza Trejo & Ernesto Bárcenas & José E. Hernández Díez & Guillermo Bossio & Gerardo Espinosa Pérez, 2018. "Open- and Short-Circuit Fault Identification for a Boost dc/dc Converter in PV MPPT Systems," Energies, MDPI, vol. 11(3), pages 1-15, March.
    2. Tehzeeb-ul Hassan & Rabeh Abbassi & Houssem Jerbi & Kashif Mehmood & Muhammad Faizan Tahir & Khalid Mehmood Cheema & Rajvikram Madurai Elavarasan & Farman Ali & Irfan Ahmad Khan, 2020. "A Novel Algorithm for MPPT of an Isolated PV System Using Push Pull Converter with Fuzzy Logic Controller," Energies, MDPI, vol. 13(15), pages 1-20, August.
    3. Mingxuan Chen & Suliang Ma & Haiyong Wan & Jianwen Wu & Yuan Jiang, 2018. "Distributed Control Strategy for DC Microgrids of Photovoltaic Energy Storage Systems in Off-Grid Operation," Energies, MDPI, vol. 11(10), pages 1-19, October.
    4. Long-Yi Chang & Yi-Nung Chung & Kuei-Hsiang Chao & Jia-Jing Kao, 2018. "Smart Global Maximum Power Point Tracking Controller of Photovoltaic Module Arrays," Energies, MDPI, vol. 11(3), pages 1-16, March.
    5. Sai Nikhil Vodapally & Mohd Hasan Ali, 2023. "Overview of Intelligent Inverters and Associated Cybersecurity Issues for a Grid-Connected Solar Photovoltaic System," Energies, MDPI, vol. 16(16), pages 1-19, August.
    6. 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.
    7. Václav Beránek & Tomáš Olšan & Martin Libra & Vladislav Poulek & Jan Sedláček & Minh-Quan Dang & Igor I. Tyukhov, 2018. "New Monitoring System for Photovoltaic Power Plants’ Management," Energies, MDPI, vol. 11(10), pages 1-13, September.
    8. Slimane Hadji & Jean-Paul Gaubert & Fateh Krim, 2018. "Real-Time Genetic Algorithms-Based MPPT: Study and Comparison (Theoretical an Experimental) with Conventional Methods," Energies, MDPI, vol. 11(2), pages 1-17, February.

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