Enhancing photovoltaic parameter estimation: integration of non-linear hunting and reinforcement learning strategies with golden jackal optimizer
Abstract The advancement of Photovoltaic (PV) systems hinges on the precise optimization of their parameters. Among the numerous optimization techniques, the effectiveness of each often rests on their inherent parameters. This research introduces a new methodology, the Reinforcement Learning-based G...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-52670-8 |
| _version_ | 1797274677642526720 |
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| author | Chappani Sankaran Sundar Ganesh Chandrasekaran Kumar Manoharan Premkumar Bizuwork Derebew |
| author_facet | Chappani Sankaran Sundar Ganesh Chandrasekaran Kumar Manoharan Premkumar Bizuwork Derebew |
| author_sort | Chappani Sankaran Sundar Ganesh |
| collection | DOAJ |
| description | Abstract The advancement of Photovoltaic (PV) systems hinges on the precise optimization of their parameters. Among the numerous optimization techniques, the effectiveness of each often rests on their inherent parameters. This research introduces a new methodology, the Reinforcement Learning-based Golden Jackal Optimizer (RL-GJO). This approach uniquely combines reinforcement learning with the Golden Jackal Optimizer to enhance its efficiency and adaptability in handling various optimization problems. Furthermore, the research incorporates an advanced non-linear hunting strategy to optimize the algorithm’s performance. The proposed algorithm is first validated using 29 CEC2017 benchmark test functions and five engineering-constrained design problems. Secondly, rigorous testing on PV parameter estimation benchmark datasets, including the single-diode model, double-diode model, three-diode model, and a representative PV module, was carried out to highlight the superiority of RL-GJO. The results were compelling: the root mean square error values achieved by RL-GJO were markedly lower than those of the original algorithm and other prevalent optimization methods. The synergy between reinforcement learning and GJO in this approach facilitates faster convergence and improved solution quality. This integration not only improves the performance metrics but also ensures a more efficient optimization process, especially in complex PV scenarios. With an average Freidman’s rank test values of 1.564 for numerical and engineering design problems and 1.742 for parameter estimation problems, the proposed RL-GJO is performing better than the original GJO and other peers. The proposed RL-GJO stands out as a reliable tool for PV parameter estimation. By seamlessly combining reinforcement learning with the golden jackal optimizer, it sets a new benchmark in PV optimization, indicating a promising avenue for future research and applications. |
| first_indexed | 2024-03-07T15:02:39Z |
| format | Article |
| id | doaj.art-bd3027081cbf40fa9fafacba3f05a27f |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:02:39Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-bd3027081cbf40fa9fafacba3f05a27f2024-03-05T19:05:35ZengNature PortfolioScientific Reports2045-23222024-02-0114115610.1038/s41598-024-52670-8Enhancing photovoltaic parameter estimation: integration of non-linear hunting and reinforcement learning strategies with golden jackal optimizerChappani Sankaran Sundar Ganesh0Chandrasekaran Kumar1Manoharan Premkumar2Bizuwork Derebew3Department of Electrical and Electronics Engineering, Karpagam College of EngineeringDepartment of Electrical and Electronics Engineering, Karpagam College of EngineeringDepartment of Electrical and Electronics Engineering, Dayananda Sagar College of EngineeringDepartment of Statistics, College of Natural and Computational Science, Mizan-Tepi UniversityAbstract The advancement of Photovoltaic (PV) systems hinges on the precise optimization of their parameters. Among the numerous optimization techniques, the effectiveness of each often rests on their inherent parameters. This research introduces a new methodology, the Reinforcement Learning-based Golden Jackal Optimizer (RL-GJO). This approach uniquely combines reinforcement learning with the Golden Jackal Optimizer to enhance its efficiency and adaptability in handling various optimization problems. Furthermore, the research incorporates an advanced non-linear hunting strategy to optimize the algorithm’s performance. The proposed algorithm is first validated using 29 CEC2017 benchmark test functions and five engineering-constrained design problems. Secondly, rigorous testing on PV parameter estimation benchmark datasets, including the single-diode model, double-diode model, three-diode model, and a representative PV module, was carried out to highlight the superiority of RL-GJO. The results were compelling: the root mean square error values achieved by RL-GJO were markedly lower than those of the original algorithm and other prevalent optimization methods. The synergy between reinforcement learning and GJO in this approach facilitates faster convergence and improved solution quality. This integration not only improves the performance metrics but also ensures a more efficient optimization process, especially in complex PV scenarios. With an average Freidman’s rank test values of 1.564 for numerical and engineering design problems and 1.742 for parameter estimation problems, the proposed RL-GJO is performing better than the original GJO and other peers. The proposed RL-GJO stands out as a reliable tool for PV parameter estimation. By seamlessly combining reinforcement learning with the golden jackal optimizer, it sets a new benchmark in PV optimization, indicating a promising avenue for future research and applications.https://doi.org/10.1038/s41598-024-52670-8 |
| spellingShingle | Chappani Sankaran Sundar Ganesh Chandrasekaran Kumar Manoharan Premkumar Bizuwork Derebew Enhancing photovoltaic parameter estimation: integration of non-linear hunting and reinforcement learning strategies with golden jackal optimizer Scientific Reports |
| title | Enhancing photovoltaic parameter estimation: integration of non-linear hunting and reinforcement learning strategies with golden jackal optimizer |
| title_full | Enhancing photovoltaic parameter estimation: integration of non-linear hunting and reinforcement learning strategies with golden jackal optimizer |
| title_fullStr | Enhancing photovoltaic parameter estimation: integration of non-linear hunting and reinforcement learning strategies with golden jackal optimizer |
| title_full_unstemmed | Enhancing photovoltaic parameter estimation: integration of non-linear hunting and reinforcement learning strategies with golden jackal optimizer |
| title_short | Enhancing photovoltaic parameter estimation: integration of non-linear hunting and reinforcement learning strategies with golden jackal optimizer |
| title_sort | enhancing photovoltaic parameter estimation integration of non linear hunting and reinforcement learning strategies with golden jackal optimizer |
| url | https://doi.org/10.1038/s41598-024-52670-8 |
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