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Sliding-Mode Controller for Maximum Power Point Tracking in Grid-Connected Photovoltaic Systems

Author

Listed:
  • Paula Andrea Ortiz Valencia

    (Instituto Tecnológico Metropolitano (ITM), Calle 73 No 76A-354 Vía al Volador, Medellín 05001000, Colombia)

  • Carlos Andres Ramos-Paja

    (Universidad Nacional de Colombia, Carrera 80 No 65-223-Block M8-113, Medellín 050041, Colombia)

Abstract

The maximum power point tracking (MPPT) of photovoltaic systems must be as fast and accurate as possible to increase the power production, which eventually increases the PV system profitability. This paper proposes and mathematically analyses a sliding-mode controller to provide a fast and accurate maximum power point tracking in grid-connected photovoltaic systems using a single control stage. This approach avoids the circular dependency in the design of classical cascade controllers used to optimize the photovoltaic system operation, and at the same time, it reduces the number of controllers and avoids the use of linearized models to provide global stability in all the operation range. Such a compact solution also reduces the system cost and implementation complexity. To ensure the stability of the proposed solution, detailed mathematical analyses are performed to demonstrate the fulfillment of the transversality, reachability and equivalent control conditions. Finally, the performance of the proposed solution is validated using detailed simulations, executed in the power electronics simulator PSIM, accounting for both environmental and load perturbations.

Suggested Citation

  • Paula Andrea Ortiz Valencia & Carlos Andres Ramos-Paja, 2015. "Sliding-Mode Controller for Maximum Power Point Tracking in Grid-Connected Photovoltaic Systems," Energies, MDPI, vol. 8(11), pages 1-25, November.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:11:p:12318-12387:d:58157
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    References listed on IDEAS

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    1. Adriana Trejos & Daniel Gonzalez & Carlos Andres Ramos-Paja, 2012. "Modeling of Step-up Grid-Connected Photovoltaic Systems for Control Purposes," Energies, MDPI, vol. 5(6), pages 1-27, June.
    2. Jin-Hyuk Park & Hae-Gwang Jeong & Kyo-Beum Lee, 2013. "Output Current Ripple Reduction Algorithms for Home Energy Storage Systems," Energies, MDPI, vol. 6(10), pages 1-18, October.
    3. Qi, Jun & Zhang, Youbing & Chen, Yi, 2014. "Modeling and maximum power point tracking (MPPT) method for PV array under partial shade conditions," Renewable Energy, Elsevier, vol. 66(C), pages 337-345.
    4. Ahmed, Jubaer & Salam, Zainal, 2014. "A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability," Applied Energy, Elsevier, vol. 119(C), pages 118-130.
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    Citations

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    Cited by:

    1. Aranzazu D. Martin & Juan M. Cano & Reyes S. Herrera & Jesus R. Vazquez, 2019. "Wireless Sliding MPPT Control of Photovoltaic Systems in Distributed Generation Systems," Energies, MDPI, vol. 12(17), pages 1-16, August.
    2. Peng, Lele & Zheng, Shubin & Chai, Xiaodong & Li, Liming, 2018. "A novel tangent error maximum power point tracking algorithm for photovoltaic system under fast multi-changing solar irradiances," Applied Energy, Elsevier, vol. 210(C), pages 303-316.
    3. Karim M. El-Sharawy & Hatem Y. Diab & Mahmoud O. Abdelsalam & Mostafa I. Marei, 2021. "A Unified Control Strategy of Distributed Generation for Grid-Connected and Islanded Operation Conditions Using an Artificial Neural Network," Sustainability, MDPI, vol. 13(11), pages 1-30, June.
    4. 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.
    5. Abdelali El Aroudi & Mohamed Al-Numay & Germain Garcia & Khalifa Al Hossani & Naji Al Sayari & Angel Cid-Pastor, 2018. "Analysis of Nonlinear Dynamics of a Quadratic Boost Converter Used for Maximum Power Point Tracking in a Grid-Interlinked PV System," Energies, MDPI, vol. 12(1), pages 1-23, December.
    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.

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