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Numerical Modelling and Digitalization Analysis for a Photovoltaic Pumping System Placed in the South of Romania

Author

Listed:
  • Laurentiu Fara

    (Department of Physics, Faculty of Applied Sciences, Polytechnic University of Bucharest, 060042 Bucharest, Romania
    Academy of Romanian Scientists, 050045 Bucharest, Romania)

  • Dan Craciunescu

    (Department of Physics, Faculty of Applied Sciences, Polytechnic University of Bucharest, 060042 Bucharest, Romania)

  • Silvian Fara

    (Department of Physics, Faculty of Applied Sciences, Polytechnic University of Bucharest, 060042 Bucharest, Romania)

Abstract

The authors studied a working off-grid type photovoltaic (PV) pumping system for irrigation use. The methodology was based on digitalization analysis and numerical modeling as a preliminary stage. A mathematical model of the PV pumping installation considered the determination of the characteristic equations for all its components. These have been used together with the SISIFO simulation software to achieve the performances of the mechanical and electrical components of an advanced PV pumping system. Its global performance features, namely the monthly energy yield, monthly pumping yield, and monthly total performances (energy and flow rate) were introduced. Digital platform (DP) for PV systems characterized by three advanced technologies—machine learning (ML), digital twin (DT) and artificial intelligence (AI) was developed. The simulation results were discussed for a specific case study conducted for a location in the Southern Romania regarding the irrigation potential, taking into account the main meteorological parameters: solar irradiance and ambient temperature, related to the site. The AI approach was implemented to achieve an optimum operation of the PV pumping system by the use of the maximum power point tracking (MPPT) method and the MATLAB/Simulink software. A unified development of all the components of the PV pumping system using the SISIFO simulation software was established by the authors, with major implications in the development of solar PV installations on large-scale.

Suggested Citation

  • Laurentiu Fara & Dan Craciunescu & Silvian Fara, 2021. "Numerical Modelling and Digitalization Analysis for a Photovoltaic Pumping System Placed in the South of Romania," Energies, MDPI, vol. 14(10), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2778-:d:553012
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    References listed on IDEAS

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    1. Poompavai, T. & Kowsalya, M., 2019. "Control and energy management strategies applied for solar photovoltaic and wind energy fed water pumping system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 108-122.
    2. Hadj Arab, A. & Benghanem, M. & Chenlo, F., 2006. "Motor-pump system modelization," Renewable Energy, Elsevier, vol. 31(7), pages 905-913.
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    Citations

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    Cited by:

    1. Juan Ignacio Herraiz & Rita Hogan Almeida & Manuel Castillo-Cagigal & Luis Narvarte, 2023. "Experimental Performance Evaluation of a PV-Powered Center-Pivot Irrigation System for a Three-Year Operation Period," Energies, MDPI, vol. 16(9), pages 1-19, April.
    2. Izabela Rojek & Dariusz Mikołajewski & Adam Mroziński & Marek Macko, 2023. "Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage," Energies, MDPI, vol. 16(18), pages 1-26, September.
    3. Dan Craciunescu & Laurentiu Fara, 2023. "Investigation of the Partial Shading Effect of Photovoltaic Panels and Optimization of Their Performance Based on High-Efficiency FLC Algorithm," Energies, MDPI, vol. 16(3), pages 1-28, January.

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