Parameter Extraction for Photovoltaic Models with Flood-Algorithm-Based Optimization
Accurately modeling photovoltaic (PV) cells is crucial for optimizing PV systems. Researchers have proposed numerous mathematical models of PV cells to facilitate the design and simulation of PV systems. Usually, a PV cell is modeled by equivalent electrical circuit models with specific parameters,...
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MDPI AG
2024-12-01
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| author | Yacine Bouali Basem Alamri |
| author_facet | Yacine Bouali Basem Alamri |
| author_sort | Yacine Bouali |
| collection | DOAJ |
| description | Accurately modeling photovoltaic (PV) cells is crucial for optimizing PV systems. Researchers have proposed numerous mathematical models of PV cells to facilitate the design and simulation of PV systems. Usually, a PV cell is modeled by equivalent electrical circuit models with specific parameters, which are often unknown; this leads to formulating an optimization problem that is addressed through metaheuristic algorithms to identify the PV cell/module parameters accurately. This paper introduces the flood algorithm (FLA), a novel and efficient optimization approach, to extract parameters for various PV models, including single-diode, double-diode, and three-diode models and PV module configurations. The FLA’s performance is systematically evaluated against nine recently developed optimization algorithms through comprehensive comparative and statistical analyses. The results highlight the FLA’s superior convergence speed, global search capability, and robustness. This study explores two distinct objective functions to enhance accuracy: one based on experimental current–voltage data and another integrating the Newton–Raphson method. Applying metaheuristic algorithms with the Newton–Raphson-based objective function reduced the root-mean-square error (RMSE) more effectively than traditional methods. These findings establish the FLA as a computationally efficient and reliable approach to PV parameter extraction, with promising implications for advancing PV system design and simulation. |
| format | Article |
| id | doaj-art-c1be060b88824a20bfc0d3ca549eb7f9 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj-art-c1be060b88824a20bfc0d3ca549eb7f92025-01-10T13:17:59ZengMDPI AGMathematics2227-73902024-12-011311910.3390/math13010019Parameter Extraction for Photovoltaic Models with Flood-Algorithm-Based OptimizationYacine Bouali0Basem Alamri1Department of Electrical Engineering, University of Science and Technology Houari Boumediene, P.O. Box 32, El-Alia, Algiers 16111, AlgeriaDepartment of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaAccurately modeling photovoltaic (PV) cells is crucial for optimizing PV systems. Researchers have proposed numerous mathematical models of PV cells to facilitate the design and simulation of PV systems. Usually, a PV cell is modeled by equivalent electrical circuit models with specific parameters, which are often unknown; this leads to formulating an optimization problem that is addressed through metaheuristic algorithms to identify the PV cell/module parameters accurately. This paper introduces the flood algorithm (FLA), a novel and efficient optimization approach, to extract parameters for various PV models, including single-diode, double-diode, and three-diode models and PV module configurations. The FLA’s performance is systematically evaluated against nine recently developed optimization algorithms through comprehensive comparative and statistical analyses. The results highlight the FLA’s superior convergence speed, global search capability, and robustness. This study explores two distinct objective functions to enhance accuracy: one based on experimental current–voltage data and another integrating the Newton–Raphson method. Applying metaheuristic algorithms with the Newton–Raphson-based objective function reduced the root-mean-square error (RMSE) more effectively than traditional methods. These findings establish the FLA as a computationally efficient and reliable approach to PV parameter extraction, with promising implications for advancing PV system design and simulation.https://www.mdpi.com/2227-7390/13/1/19flood algorithmparameters extractionphotovoltaic cell modelmetaheuristicNewton–Raphson |
| spellingShingle | Yacine Bouali Basem Alamri Parameter Extraction for Photovoltaic Models with Flood-Algorithm-Based Optimization Mathematics flood algorithm parameters extraction photovoltaic cell model metaheuristic Newton–Raphson |
| title | Parameter Extraction for Photovoltaic Models with Flood-Algorithm-Based Optimization |
| title_full | Parameter Extraction for Photovoltaic Models with Flood-Algorithm-Based Optimization |
| title_fullStr | Parameter Extraction for Photovoltaic Models with Flood-Algorithm-Based Optimization |
| title_full_unstemmed | Parameter Extraction for Photovoltaic Models with Flood-Algorithm-Based Optimization |
| title_short | Parameter Extraction for Photovoltaic Models with Flood-Algorithm-Based Optimization |
| title_sort | parameter extraction for photovoltaic models with flood algorithm based optimization |
| topic | flood algorithm parameters extraction photovoltaic cell model metaheuristic Newton–Raphson |
| url | https://www.mdpi.com/2227-7390/13/1/19 |
| work_keys_str_mv | AT yacinebouali parameterextractionforphotovoltaicmodelswithfloodalgorithmbasedoptimization AT basemalamri parameterextractionforphotovoltaicmodelswithfloodalgorithmbasedoptimization |