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Grid-Connected PV Systems Controlled by Sliding via Wireless Communication

Author

Listed:
  • Juan M. Cano

    (Department of Electrical and Thermal Engineering, Design and Projects, University of Huelva, 21007 Huelva, Spain)

  • Aranzazu D. Martin

    (Department of Electrical and Thermal Engineering, Design and Projects, University of Huelva, 21007 Huelva, Spain)

  • Reyes S. Herrera

    (Department of Electrical and Thermal Engineering, Design and Projects, University of Huelva, 21007 Huelva, Spain)

  • Jesus R. Vazquez

    (Department of Electrical and Thermal Engineering, Design and Projects, University of Huelva, 21007 Huelva, Spain)

  • Francisco Javier Ruiz-Rodriguez

    (Department of Electrical and Thermal Engineering, Design and Projects, University of Huelva, 21007 Huelva, Spain)

Abstract

Grid-connected photovoltaic (PV) systems are designed to provide energy to the grid. This energy transfer must fulfil some requirements such as system stability, power quality and reliability. Thus, the aim of this work is to design and control a grid-connected PV system via wireless to guarantee the correct operation of the system. It is crucial to monitor and supervise the system to control and/or detect faults in real time and in a remote way. To do that, the DC/DC converter and the DC/AC converter of the grid-connected PV system are controlled wirelessly, reducing costs in cabling installations. The used control methods are the sliding for the DC/DC converter and the Proportional-Integral (PI) for the inverter. The sliding control is robust, ensures system stability under perturbations, and is proven to work well via wireless. The PI control is simple and effective, proving its validity through wireless too. In addition, the effect of the communications is analysed in both controllers. An experimental platform has been built to conduct the experiments to verify the operation of the grid-connected PV system remotely. The results show that the system operates well, achieving the desired values for the maximum power point tracker (MPPT) sliding control and the energy transfer from the inverter to the grid.

Suggested Citation

  • Juan M. Cano & Aranzazu D. Martin & Reyes S. Herrera & Jesus R. Vazquez & Francisco Javier Ruiz-Rodriguez, 2021. "Grid-Connected PV Systems Controlled by Sliding via Wireless Communication," Energies, MDPI, vol. 14(7), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1931-:d:527528
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    References listed on IDEAS

    as
    1. Sufyan Samara & Emad Natsheh, 2020. "Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems," Sustainability, MDPI, vol. 12(5), pages 1-20, March.
    2. 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.
    3. Lyden, S. & Haque, M.E., 2015. "Maximum Power Point Tracking techniques for photovoltaic systems: A comprehensive review and comparative analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1504-1518.
    4. Papadakis, Kostas & Koutroulis, Eftichios & Kalaitzakis, Kostas, 2005. "A server database system for remote monitoring and operational evaluation of renewable energy sources plants," Renewable Energy, Elsevier, vol. 30(11), pages 1649-1669.
    5. Jun-Hyun Shin & Jin-O Kim, 2020. "On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant," Energies, MDPI, vol. 13(17), pages 1-12, September.
    6. Qiang Zhao & Shuai Shao & Lingxing Lu & Xin Liu & Honglu Zhu, 2018. "A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm," Energies, MDPI, vol. 11(1), pages 1-21, January.
    7. Chen, Zhicong & Wu, Lijun & Cheng, Shuying & Lin, Peijie & Wu, Yue & Lin, Wencheng, 2017. "Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics," Applied Energy, Elsevier, vol. 204(C), pages 912-931.
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