Grey wolf‐based heuristic methods for accurate parameter extraction to optimize the performance of PV modules
Abstract Parameter prediction for PV solar cells plays a crucial role in controlling and optimizing the performance of PV modules. In this study, the parameter prediction of a four‐diode PV model was carried out using the Improved Grey Wolf Optimization (IGWO) algorithm, which builds upon the Grey W...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.13061 |
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| author | Seyit Alperen Celtek Seda Kul Manish Kumar Singla Jyoti Gupta Murodbek Safaraliev Hamed Zeinoddini‐Meymand |
| author_facet | Seyit Alperen Celtek Seda Kul Manish Kumar Singla Jyoti Gupta Murodbek Safaraliev Hamed Zeinoddini‐Meymand |
| author_sort | Seyit Alperen Celtek |
| collection | DOAJ |
| description | Abstract Parameter prediction for PV solar cells plays a crucial role in controlling and optimizing the performance of PV modules. In this study, the parameter prediction of a four‐diode PV model was carried out using the Improved Grey Wolf Optimization (IGWO) algorithm, which builds upon the Grey Wolf Optimization (GWO) algorithm. The parameters required for the four‐diode PV model were optimized based on a predefined objective function. Subsequently, the obtained data were compared with the data from RTCFrance Solar Cell to validate the accuracy and reliability of the optimization results. The evaluation of the optimization results revealed that the Sum Square Error (SSE) values for PSOGWO, AGWOCS, GWOCS, and GWO were 3.96E‐05, while the MSE value for IGWO was 3.6309E‐05. These findings clearly demonstrate that the proposed IGWO algorithm outperforms the other algorithms used in the study, based on the minimized SSE values. This study emphasizes the importance of parameter prediction in optimizing PV performance, and it contributes to thefield by introducing the novel IGWO algorithm for the four‐diode PV model. The algorithm's superior performance, as demonstrated through extensive testing and comparison with existing algorithms, validates its efficacy in accurately predicting the parameters for the PV solar cell model. |
| format | Article |
| id | doaj-art-44fd416949de42e99daeb2a3fc5a8988 |
| institution | OA Journals |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-44fd416949de42e99daeb2a3fc5a89882025-08-20T02:37:19ZengWileyIET Renewable Power Generation1752-14161752-14242024-10-0118142248226010.1049/rpg2.13061Grey wolf‐based heuristic methods for accurate parameter extraction to optimize the performance of PV modulesSeyit Alperen Celtek0Seda Kul1Manish Kumar Singla2Jyoti Gupta3Murodbek Safaraliev4Hamed Zeinoddini‐Meymand5Department of Energy Systems Engineering Engineering Faculty Karamanoglu Mehmetbey University Karaman TurkeyDepartment of Electric and Electronic Engineering Engineering Faculty Karamanoglu Mehmetbey University Karaman TurkeyDepartment of Interdisciplinary Courses in Engineering, Chitkara University Institute of Engineering and Technology Chitkara University Punjab IndiaSchool of Engineering and Technology K. R. Mangalam University Gurugram Haryana IndiaDepartment of Automated Electrical Systems Ural Federal University Yekaterinburg RussiaDepartment of Electrical and Computer Engineering Graduate University of Advanced Technology Kerman IranAbstract Parameter prediction for PV solar cells plays a crucial role in controlling and optimizing the performance of PV modules. In this study, the parameter prediction of a four‐diode PV model was carried out using the Improved Grey Wolf Optimization (IGWO) algorithm, which builds upon the Grey Wolf Optimization (GWO) algorithm. The parameters required for the four‐diode PV model were optimized based on a predefined objective function. Subsequently, the obtained data were compared with the data from RTCFrance Solar Cell to validate the accuracy and reliability of the optimization results. The evaluation of the optimization results revealed that the Sum Square Error (SSE) values for PSOGWO, AGWOCS, GWOCS, and GWO were 3.96E‐05, while the MSE value for IGWO was 3.6309E‐05. These findings clearly demonstrate that the proposed IGWO algorithm outperforms the other algorithms used in the study, based on the minimized SSE values. This study emphasizes the importance of parameter prediction in optimizing PV performance, and it contributes to thefield by introducing the novel IGWO algorithm for the four‐diode PV model. The algorithm's superior performance, as demonstrated through extensive testing and comparison with existing algorithms, validates its efficacy in accurately predicting the parameters for the PV solar cell model.https://doi.org/10.1049/rpg2.13061diodesoptimisationparameter estimationphotovoltaic cellssolar photovoltaic systems |
| spellingShingle | Seyit Alperen Celtek Seda Kul Manish Kumar Singla Jyoti Gupta Murodbek Safaraliev Hamed Zeinoddini‐Meymand Grey wolf‐based heuristic methods for accurate parameter extraction to optimize the performance of PV modules IET Renewable Power Generation diodes optimisation parameter estimation photovoltaic cells solar photovoltaic systems |
| title | Grey wolf‐based heuristic methods for accurate parameter extraction to optimize the performance of PV modules |
| title_full | Grey wolf‐based heuristic methods for accurate parameter extraction to optimize the performance of PV modules |
| title_fullStr | Grey wolf‐based heuristic methods for accurate parameter extraction to optimize the performance of PV modules |
| title_full_unstemmed | Grey wolf‐based heuristic methods for accurate parameter extraction to optimize the performance of PV modules |
| title_short | Grey wolf‐based heuristic methods for accurate parameter extraction to optimize the performance of PV modules |
| title_sort | grey wolf based heuristic methods for accurate parameter extraction to optimize the performance of pv modules |
| topic | diodes optimisation parameter estimation photovoltaic cells solar photovoltaic systems |
| url | https://doi.org/10.1049/rpg2.13061 |
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