Modeling solar power plants with daily data using genetic programming and equivalent circuit
Abstract Among the various methods proposed for modeling solar panels, those based on equivalent circuits have received significant attention. In these approaches, determining unknown parameters varies depending on the modeling objective. To model voltage–current characteristics, circuit analysis me...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.13162 |
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| author | Alireza Reisi Abbas‐Ali Zamani Seyyed Masoud Barakati |
| author_facet | Alireza Reisi Abbas‐Ali Zamani Seyyed Masoud Barakati |
| author_sort | Alireza Reisi |
| collection | DOAJ |
| description | Abstract Among the various methods proposed for modeling solar panels, those based on equivalent circuits have received significant attention. In these approaches, determining unknown parameters varies depending on the modeling objective. To model voltage–current characteristics, circuit analysis methods are employed to extract these unknown parameters. However, this modeling method relies on data provided by the solar panel manufacturer, leading to increased modeling error over time as coefficients change. In this article, a method independent of the manufacturer's data for modeling solar panels is presented. This method enables accurate modeling of pre‐installed solar power plants. By utilizing genetic programming on a single day's worth of data from a solar panel, the proposed method can establish relationships with a high degree of fit for the open‐circuit voltage, maximum power point, and short‐circuit current based on weather conditions. Through these relationships, the voltage–current characteristics can be modeled with greater precision compared to traditional circuit analysis methods, and without the need for data from the solar panel manufacturer. Finally, for further evaluation, a 3 kW solar power plant is modeled, which demonstrates the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-a88d1cd413b64ba0a2fc727df1acc17b |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-a88d1cd413b64ba0a2fc727df1acc17b2025-01-30T12:15:54ZengWileyIET Renewable Power Generation1752-14161752-14242024-12-0118164222423210.1049/rpg2.13162Modeling solar power plants with daily data using genetic programming and equivalent circuitAlireza Reisi0Abbas‐Ali Zamani1Seyyed Masoud Barakati2Department of Electrical Engineering Technical and Vocational University (TVU) Tehran IranDepartment of Electrical Engineering Technical and Vocational University (TVU) Tehran IranElectrical and Computer Department University of Sistan and Baluchestan Zahedan IranAbstract Among the various methods proposed for modeling solar panels, those based on equivalent circuits have received significant attention. In these approaches, determining unknown parameters varies depending on the modeling objective. To model voltage–current characteristics, circuit analysis methods are employed to extract these unknown parameters. However, this modeling method relies on data provided by the solar panel manufacturer, leading to increased modeling error over time as coefficients change. In this article, a method independent of the manufacturer's data for modeling solar panels is presented. This method enables accurate modeling of pre‐installed solar power plants. By utilizing genetic programming on a single day's worth of data from a solar panel, the proposed method can establish relationships with a high degree of fit for the open‐circuit voltage, maximum power point, and short‐circuit current based on weather conditions. Through these relationships, the voltage–current characteristics can be modeled with greater precision compared to traditional circuit analysis methods, and without the need for data from the solar panel manufacturer. Finally, for further evaluation, a 3 kW solar power plant is modeled, which demonstrates the effectiveness of the proposed method.https://doi.org/10.1049/rpg2.13162numerical analysisphotovoltaic power systemspower system analysis computingpower system identificationpower system parameter estimationpower system simulation |
| spellingShingle | Alireza Reisi Abbas‐Ali Zamani Seyyed Masoud Barakati Modeling solar power plants with daily data using genetic programming and equivalent circuit IET Renewable Power Generation numerical analysis photovoltaic power systems power system analysis computing power system identification power system parameter estimation power system simulation |
| title | Modeling solar power plants with daily data using genetic programming and equivalent circuit |
| title_full | Modeling solar power plants with daily data using genetic programming and equivalent circuit |
| title_fullStr | Modeling solar power plants with daily data using genetic programming and equivalent circuit |
| title_full_unstemmed | Modeling solar power plants with daily data using genetic programming and equivalent circuit |
| title_short | Modeling solar power plants with daily data using genetic programming and equivalent circuit |
| title_sort | modeling solar power plants with daily data using genetic programming and equivalent circuit |
| topic | numerical analysis photovoltaic power systems power system analysis computing power system identification power system parameter estimation power system simulation |
| url | https://doi.org/10.1049/rpg2.13162 |
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