Identification of photovoltaic module parameters by implementing a novel teaching learning based optimization with unique exemplar generation scheme (TLBO-UEGS)
The performance evaluation of a Photovoltaic (PV) system heavily relies on accurately estimating the parameters based on its current—voltage relationships. However, due to the PV model’s inherent complexity, obtaining these parameters with precision and efficiency is a challenging task. In this stud...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Energy Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723011605 |
| _version_ | 1797378658869968896 |
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| author | Abhishek Sharma Wei Hong Lim El-Sayed M. El-Kenawy Sew Sun Tiang Ashok Singh Bhandari Amal H. Alharbi Doaa Sami Khafaga |
| author_facet | Abhishek Sharma Wei Hong Lim El-Sayed M. El-Kenawy Sew Sun Tiang Ashok Singh Bhandari Amal H. Alharbi Doaa Sami Khafaga |
| author_sort | Abhishek Sharma |
| collection | DOAJ |
| description | The performance evaluation of a Photovoltaic (PV) system heavily relies on accurately estimating the parameters based on its current—voltage relationships. However, due to the PV model’s inherent complexity, obtaining these parameters with precision and efficiency is a challenging task. In this study, a new variant known as teaching learning-based optimization with unique exemplar generation schemes (TLBO-UEGS) is proposed to address PV module parameter estimation problems with robustness and effectiveness. To enhance the performance of TLBO-UEGS, a modified initialization scheme that leverages the strengths of chaotic maps and dynamic oppositional based learning is introduced. This scheme ensures the generation of an initial population with improved solution quality. Furthermore, both the modified teacher phase and modified learner phase are integrated within the TLBO-UEGS optimization framework. This integration allows for different learning strategies to be employed based on the fitness values of each learner, effectively updating their search trajectories. Within the modified teacher phase, two unique exemplar generation schemes are designed to facilitate more effective guidance for learners in the first half of the population while maintaining population diversity. Meanwhile, the modified learner phase emulates a realistic knowledge acquisition process by enabling learners in the second half of the population to engage in collaborative learning with multiple peer learners or retain valuable knowledge from previous learning processes. Extensive simulations demonstrate that TLBO-UEGS achieves superior results, with the minimum root mean square error (RMSE) values of 3.5644 × 10−04 ± 0.0014, 1.3237 × 10−04 ± 0.0043, and 6.6016 × 10−06 ± 0.00011 obtained for Photowatt-PWP201, Leibold Solar (LSM 20), and Leybold Solar (STE 4/100) PV modules, respectively. |
| first_indexed | 2024-03-08T20:10:51Z |
| format | Article |
| id | doaj.art-244d27234e7242fa8e5115d1852ee0d7 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-08T20:10:51Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-244d27234e7242fa8e5115d1852ee0d72023-12-23T05:21:24ZengElsevierEnergy Reports2352-48472023-11-011014851506Identification of photovoltaic module parameters by implementing a novel teaching learning based optimization with unique exemplar generation scheme (TLBO-UEGS)Abhishek Sharma0Wei Hong Lim1El-Sayed M. El-Kenawy2Sew Sun Tiang3Ashok Singh Bhandari4Amal H. Alharbi5Doaa Sami Khafaga6Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun 248002, IndiaFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia; Corresponding author.Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, EgyptFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, MalaysiaDepartment of Mathematics School of Basic & Applied Sciences Shri Guru Ram Rai University, Dehradun 248002, IndiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaThe performance evaluation of a Photovoltaic (PV) system heavily relies on accurately estimating the parameters based on its current—voltage relationships. However, due to the PV model’s inherent complexity, obtaining these parameters with precision and efficiency is a challenging task. In this study, a new variant known as teaching learning-based optimization with unique exemplar generation schemes (TLBO-UEGS) is proposed to address PV module parameter estimation problems with robustness and effectiveness. To enhance the performance of TLBO-UEGS, a modified initialization scheme that leverages the strengths of chaotic maps and dynamic oppositional based learning is introduced. This scheme ensures the generation of an initial population with improved solution quality. Furthermore, both the modified teacher phase and modified learner phase are integrated within the TLBO-UEGS optimization framework. This integration allows for different learning strategies to be employed based on the fitness values of each learner, effectively updating their search trajectories. Within the modified teacher phase, two unique exemplar generation schemes are designed to facilitate more effective guidance for learners in the first half of the population while maintaining population diversity. Meanwhile, the modified learner phase emulates a realistic knowledge acquisition process by enabling learners in the second half of the population to engage in collaborative learning with multiple peer learners or retain valuable knowledge from previous learning processes. Extensive simulations demonstrate that TLBO-UEGS achieves superior results, with the minimum root mean square error (RMSE) values of 3.5644 × 10−04 ± 0.0014, 1.3237 × 10−04 ± 0.0043, and 6.6016 × 10−06 ± 0.00011 obtained for Photowatt-PWP201, Leibold Solar (LSM 20), and Leybold Solar (STE 4/100) PV modules, respectively.http://www.sciencedirect.com/science/article/pii/S2352484723011605Single-diode modelParameter estimationOptimizationTeaching learning-based optimization |
| spellingShingle | Abhishek Sharma Wei Hong Lim El-Sayed M. El-Kenawy Sew Sun Tiang Ashok Singh Bhandari Amal H. Alharbi Doaa Sami Khafaga Identification of photovoltaic module parameters by implementing a novel teaching learning based optimization with unique exemplar generation scheme (TLBO-UEGS) Energy Reports Single-diode model Parameter estimation Optimization Teaching learning-based optimization |
| title | Identification of photovoltaic module parameters by implementing a novel teaching learning based optimization with unique exemplar generation scheme (TLBO-UEGS) |
| title_full | Identification of photovoltaic module parameters by implementing a novel teaching learning based optimization with unique exemplar generation scheme (TLBO-UEGS) |
| title_fullStr | Identification of photovoltaic module parameters by implementing a novel teaching learning based optimization with unique exemplar generation scheme (TLBO-UEGS) |
| title_full_unstemmed | Identification of photovoltaic module parameters by implementing a novel teaching learning based optimization with unique exemplar generation scheme (TLBO-UEGS) |
| title_short | Identification of photovoltaic module parameters by implementing a novel teaching learning based optimization with unique exemplar generation scheme (TLBO-UEGS) |
| title_sort | identification of photovoltaic module parameters by implementing a novel teaching learning based optimization with unique exemplar generation scheme tlbo uegs |
| topic | Single-diode model Parameter estimation Optimization Teaching learning-based optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723011605 |
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