Numerical Modelling and Digitalization Analysis for a Photovoltaic Pumping System Placed in the South of Romania
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...
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| Format: | Article |
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MDPI AG
2021-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/10/2778 |
| _version_ | 1797534491181318144 |
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| author | Laurentiu Fara Dan Craciunescu Silvian Fara |
| author_facet | Laurentiu Fara Dan Craciunescu Silvian Fara |
| author_sort | Laurentiu Fara |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:30:16Z |
| format | Article |
| id | doaj.art-faa2e3d4f8eb47d8bb54c0f2031fc7a2 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T11:30:16Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-faa2e3d4f8eb47d8bb54c0f2031fc7a22023-11-21T19:21:25ZengMDPI AGEnergies1996-10732021-05-011410277810.3390/en14102778Numerical Modelling and Digitalization Analysis for a Photovoltaic Pumping System Placed in the South of RomaniaLaurentiu Fara0Dan Craciunescu1Silvian Fara2Department of Physics, Faculty of Applied Sciences, Polytechnic University of Bucharest, 060042 Bucharest, RomaniaDepartment of Physics, Faculty of Applied Sciences, Polytechnic University of Bucharest, 060042 Bucharest, RomaniaDepartment of Physics, Faculty of Applied Sciences, Polytechnic University of Bucharest, 060042 Bucharest, RomaniaThe 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.https://www.mdpi.com/1996-1073/14/10/2778photovoltaic pumping systemsimulation toolsdigital platformperformancesmeteorological parametersartificial intelligence (AI) |
| spellingShingle | Laurentiu Fara Dan Craciunescu Silvian Fara Numerical Modelling and Digitalization Analysis for a Photovoltaic Pumping System Placed in the South of Romania Energies photovoltaic pumping system simulation tools digital platform performances meteorological parameters artificial intelligence (AI) |
| title | Numerical Modelling and Digitalization Analysis for a Photovoltaic Pumping System Placed in the South of Romania |
| title_full | Numerical Modelling and Digitalization Analysis for a Photovoltaic Pumping System Placed in the South of Romania |
| title_fullStr | Numerical Modelling and Digitalization Analysis for a Photovoltaic Pumping System Placed in the South of Romania |
| title_full_unstemmed | Numerical Modelling and Digitalization Analysis for a Photovoltaic Pumping System Placed in the South of Romania |
| title_short | Numerical Modelling and Digitalization Analysis for a Photovoltaic Pumping System Placed in the South of Romania |
| title_sort | numerical modelling and digitalization analysis for a photovoltaic pumping system placed in the south of romania |
| topic | photovoltaic pumping system simulation tools digital platform performances meteorological parameters artificial intelligence (AI) |
| url | https://www.mdpi.com/1996-1073/14/10/2778 |
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