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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Main Authors: Alireza Reisi, Abbas‐Ali Zamani, Seyyed Masoud Barakati
Format: Article
Language:English
Published: Wiley 2024-12-01
Series:IET Renewable Power Generation
Subjects:
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.
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institution Kabale University
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publishDate 2024-12-01
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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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AT abbasalizamani modelingsolarpowerplantswithdailydatausinggeneticprogrammingandequivalentcircuit
AT seyyedmasoudbarakati modelingsolarpowerplantswithdailydatausinggeneticprogrammingandequivalentcircuit