Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar module
An enhanced version of the moth flame optimization algorithm is proposed in this paper for rapid and precise parameter extraction of solar cells. The proposed OBLVMFO algorithm’s novelty lies primarily in the improved search strategies, where two modifications are proposed to maintain a proper balan...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722008587 |
| _version_ | 1828011072501579776 |
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| author | Abhishek Sharma Abhinav Sharma Moshe Averbukh Shailendra Rajput Vibhu Jately Sushabhan Choudhury Brian Azzopardi |
| author_facet | Abhishek Sharma Abhinav Sharma Moshe Averbukh Shailendra Rajput Vibhu Jately Sushabhan Choudhury Brian Azzopardi |
| author_sort | Abhishek Sharma |
| collection | DOAJ |
| description | An enhanced version of the moth flame optimization algorithm is proposed in this paper for rapid and precise parameter extraction of solar cells. The proposed OBLVMFO algorithm’s novelty lies primarily in the improved search strategies, where two modifications are proposed to maintain a proper balance between exploration and exploitation. Firstly, an opposition-based learning mechanism is employed to initialize the search population for the purpose of enhancing the global search. Secondly, Lévy flight distribution is used to prevent the stagnation of solutions in local minima. The implementation of intelligent rules such as OBL and Lévy flight distribution significantly improves the performance of the standard MFO. The developed OBLVMFO performed adequately and is reliable in terms of RMSE compared to other methodologies such as MFO, ALO, SCA, MRFO, and WOA. The best optimized value of RMSE achieved by OBLVMFO is 6.060E−04, 1.3600E−05, and 7.0001E−06 for STE 4/100 (polycrystalline), LSM 20 (monocrystalline), and SS2018P (polycrystalline) PV modules, respectively. The experiments performed on the benchmark test function revealed that the OBLVMFO has a 61% faster convergence speed than the standard version of MFO, which improves solution accuracy. In addition to this, two non-parametric tests: Friedman ranking and Wilcoxon rank sum are performed for the validation. |
| first_indexed | 2024-04-10T09:10:46Z |
| format | Article |
| id | doaj.art-366c5b074d214af199b313941452d1d0 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T09:10:46Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-366c5b074d214af199b313941452d1d02023-02-21T05:11:34ZengElsevierEnergy Reports2352-48472022-11-01865766592Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar moduleAbhishek Sharma0Abhinav Sharma1Moshe Averbukh2Shailendra Rajput3Vibhu Jately4Sushabhan Choudhury5Brian Azzopardi6Department of Electrical and Electronic Engineering, Ariel University, Ariel 40700, Israel; Department of Research & Development, University of Petroleum and Energy Studies, Dehradun 248007, IndiaDepartment of Electrical and Electronic Engineering, University of Petroleum and Energy Studies, Dehradun 248007, India; Corresponding author.Department of Electrical and Electronic Engineering, Ariel University, Ariel 40700, IsraelDepartment of Electrical and Electronic Engineering, Ariel University, Ariel 40700, Israel; Department of Physics, University Centre for Research and Development, Chandigarh University, Mohali 140431, IndiaDepartment of Electrical and Electronic Engineering, University of Petroleum and Energy Studies, Dehradun 248007, IndiaDepartment of Electrical and Electronic Engineering, University of Petroleum and Energy Studies, Dehradun 248007, IndiaMCAST Energy Research Group (MCAST Energy), Institute of Engineering and Transport, Malta College of Arts, Science and Technology (MCAST), Triq Kordin, Paola PLA, 9032, MaltaAn enhanced version of the moth flame optimization algorithm is proposed in this paper for rapid and precise parameter extraction of solar cells. The proposed OBLVMFO algorithm’s novelty lies primarily in the improved search strategies, where two modifications are proposed to maintain a proper balance between exploration and exploitation. Firstly, an opposition-based learning mechanism is employed to initialize the search population for the purpose of enhancing the global search. Secondly, Lévy flight distribution is used to prevent the stagnation of solutions in local minima. The implementation of intelligent rules such as OBL and Lévy flight distribution significantly improves the performance of the standard MFO. The developed OBLVMFO performed adequately and is reliable in terms of RMSE compared to other methodologies such as MFO, ALO, SCA, MRFO, and WOA. The best optimized value of RMSE achieved by OBLVMFO is 6.060E−04, 1.3600E−05, and 7.0001E−06 for STE 4/100 (polycrystalline), LSM 20 (monocrystalline), and SS2018P (polycrystalline) PV modules, respectively. The experiments performed on the benchmark test function revealed that the OBLVMFO has a 61% faster convergence speed than the standard version of MFO, which improves solution accuracy. In addition to this, two non-parametric tests: Friedman ranking and Wilcoxon rank sum are performed for the validation.http://www.sciencedirect.com/science/article/pii/S2352484722008587MFOOBLVMFOLévy flightOBLParameter optimization |
| spellingShingle | Abhishek Sharma Abhinav Sharma Moshe Averbukh Shailendra Rajput Vibhu Jately Sushabhan Choudhury Brian Azzopardi Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar module Energy Reports MFO OBLVMFO Lévy flight OBL Parameter optimization |
| title | Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar module |
| title_full | Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar module |
| title_fullStr | Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar module |
| title_full_unstemmed | Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar module |
| title_short | Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar module |
| title_sort | improved moth flame optimization algorithm based on opposition based learning and levy flight distribution for parameter estimation of solar module |
| topic | MFO OBLVMFO Lévy flight OBL Parameter optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722008587 |
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