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New Monitoring System for Photovoltaic Power Plants’ Management

Author

Listed:
  • Václav Beránek

    (Solarmonitoring, Ltd., 14700 Prague, Czech Republic)

  • Tomáš Olšan

    (Department of Physics, Czech University of Life Sciences Prague, Kamycka 129, 16500 Prague, Czech Republic)

  • Martin Libra

    (Department of Physics, Czech University of Life Sciences Prague, Kamycka 129, 16500 Prague, Czech Republic)

  • Vladislav Poulek

    (Department of Physics, Czech University of Life Sciences Prague, Kamycka 129, 16500 Prague, Czech Republic)

  • Jan Sedláček

    (Department of Physics, Czech University of Life Sciences Prague, Kamycka 129, 16500 Prague, Czech Republic)

  • Minh-Quan Dang

    (Department of Physics, Czech University of Life Sciences Prague, Kamycka 129, 16500 Prague, Czech Republic)

  • Igor I. Tyukhov

    (Department of Mechanical Engineering, San Jose State University, One Washington Square, San Jose, CA 95192-0087, USA)

Abstract

An innovative solar monitoring system has been developed. The system aimed at measuring the main parameters and characteristics of solar plants; collecting, diagnosing and processing data. The system communicates with the inverters, electrometers, metrological equipment and additional components of the photovoltaic arrays. The developed and constructed long working system is built on special data collecting technologies. At the generating plants, a special data logger BBbox is installed. The new monitoring system has been used to follow 65 solar plants in the Czech Republic and elsewhere for 175 MWp. As an example, we have selected 13 PV plants in this paper that are at least seven years old. The monitoring system contributes to quality management of plants, and it also provides data for scientific purposes. Production of electricity in the built PV plants reflects the expected values according to internationally used software PVGIS (version 5) during the previous seven years of operation. A comparison of important system parameters clearly shows the new solutions and benefits of the new Solarmon-2.0 monitoring system. Secured communications will increase data protection. A higher frequency of data saving allows higher accuracy of the mathematical models.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2495-:d:171055
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    References listed on IDEAS

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    1. Rahman, M.Mahbubur & Selvaraj, J. & Rahim, N.A. & Hasanuzzaman, M., 2018. "Global modern monitoring systems for PV based power generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 4142-4158.
    2. Jian Zhao & Xuesong Zhou & Youjie Ma & Yiqi Liu, 2017. "Analysis of Dynamic Characteristic for Solar Arrays in Series and Global Maximum Power Point Tracking Based on Optimal Initial Value Incremental Conductance Strategy under Partially Shaded Conditions," Energies, MDPI, vol. 10(1), pages 1-23, January.
    3. Marian Liberos & Raúl González-Medina & Gabriel Garcerá & Emilio Figueres, 2017. "Modelling and Control of Parallel-Connected Transformerless Inverters for Large Photovoltaic Farms," Energies, MDPI, vol. 10(8), pages 1-25, August.
    4. Madeti, Siva Ramakrishna & Singh, S.N., 2017. "Monitoring system for photovoltaic plants: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 1180-1207.
    5. 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.
    6. Boonmee, Chaiyant & Plangklang, Boonyang & Watjanatepin, Napat, 2009. "System performance of a three-phase PV-grid-connected system installed in Thailand: Data monitored analysis," Renewable Energy, Elsevier, vol. 34(2), pages 384-389.
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    Cited by:

    1. Pavel Kuznetsov & Dmitry Kotelnikov & Leonid Yuferev & Vladimir Panchenko & Vadim Bolshev & Marek Jasiński & Aymen Flah, 2022. "Method for the Automated Inspection of the Surfaces of Photovoltaic Modules," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    2. Anastasios Dounis, 2019. "Special Issue “Intelligent Control in Energy Systems”," Energies, MDPI, vol. 12(15), pages 1-9, August.
    3. Gabriella-Stefánia Szabó & Róbert Szabó & Loránd Szabó, 2022. "A Review of the Mitigating Methods against the Energy Conversion Decrease in Solar Panels," Energies, MDPI, vol. 15(18), pages 1-21, September.
    4. Tomasz Popławski & Sebastian Dudzik & Piotr Szeląg & Janusz Baran, 2021. "A Case Study of a Virtual Power Plant (VPP) as a Data Acquisition Tool for PV Energy Forecasting," Energies, MDPI, vol. 14(19), pages 1-24, September.

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