Grey wolf optimization and differential evolution-based maximum power point tracking controller for photovoltaic systems under partial shading conditions
Photovoltaic (PV) energy is one of the most abundant energy in the world for generating huge electrical power to meet the desired load. However, the arduous task in the electrical industry is to contribute to the uninterrupted power supply by the PV system as a result of partial shading conditions (...
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Taylor & Francis
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/37278/ http://umpir.ump.edu.my/id/eprint/37278/1/Grey%20wolf%20optimization%20and%20differential%20evolution-based%20maximum%20power%20.pdf http://umpir.ump.edu.my/id/eprint/37278/2/Grey%20wolf%20optimization%20and%20differential%20evolution-based.pdf |
| _version_ | 1848825208768561152 |
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| author | Kishore, D. J. Krishna Mohamed, M. R. Sudhakar, K. Peddakapu, K. |
| author_facet | Kishore, D. J. Krishna Mohamed, M. R. Sudhakar, K. Peddakapu, K. |
| author_sort | Kishore, D. J. Krishna |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Photovoltaic (PV) energy is one of the most abundant energy in the world for generating huge electrical power to meet the desired load. However, the arduous task in the electrical industry is to contribute to the uninterrupted power supply by the PV system as a result of partial shading conditions (PSC). To track the global maximum peak power (GMPP) instead of local maxima peak power (LMPP), the combination of gray wolf optimization (GWO) and differential evolution (DE) algorithm is hybridized (GWO-DE) in this work. Furthermore, the proposed system is developed in the MATLAB/Simulink software. The system is investigated under distinct atmospheric conditions and compared its performance with other studied approaches. The simulation results disclose that the hybrid GWO-DE approach shows a greater performance as compared to other studied methods with respect to convergence time, accuracy, extracted power, and efficiency. Moreover, the proposed system is developed experimentally and tested in four different cases. The outcomes of the GMPP are 984.65 W at 0.08 sec for case 1, 630.39 W at 0.08 sec for case 2, 602.56 W at 0.07 sec for case 3, and 650.08 W at 0.05 sec for case 4. It is found that the suggested hybrid GWO-DE method ensures a greater performance than other studied methods. |
| first_indexed | 2025-11-15T03:25:17Z |
| format | Article |
| id | ump-37278 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:25:17Z |
| publishDate | 2022 |
| publisher | Taylor & Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-372782023-03-14T04:45:17Z http://umpir.ump.edu.my/id/eprint/37278/ Grey wolf optimization and differential evolution-based maximum power point tracking controller for photovoltaic systems under partial shading conditions Kishore, D. J. Krishna Mohamed, M. R. Sudhakar, K. Peddakapu, K. TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Photovoltaic (PV) energy is one of the most abundant energy in the world for generating huge electrical power to meet the desired load. However, the arduous task in the electrical industry is to contribute to the uninterrupted power supply by the PV system as a result of partial shading conditions (PSC). To track the global maximum peak power (GMPP) instead of local maxima peak power (LMPP), the combination of gray wolf optimization (GWO) and differential evolution (DE) algorithm is hybridized (GWO-DE) in this work. Furthermore, the proposed system is developed in the MATLAB/Simulink software. The system is investigated under distinct atmospheric conditions and compared its performance with other studied approaches. The simulation results disclose that the hybrid GWO-DE approach shows a greater performance as compared to other studied methods with respect to convergence time, accuracy, extracted power, and efficiency. Moreover, the proposed system is developed experimentally and tested in four different cases. The outcomes of the GMPP are 984.65 W at 0.08 sec for case 1, 630.39 W at 0.08 sec for case 2, 602.56 W at 0.07 sec for case 3, and 650.08 W at 0.05 sec for case 4. It is found that the suggested hybrid GWO-DE method ensures a greater performance than other studied methods. Taylor & Francis 2022 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/37278/1/Grey%20wolf%20optimization%20and%20differential%20evolution-based%20maximum%20power%20.pdf pdf en http://umpir.ump.edu.my/id/eprint/37278/2/Grey%20wolf%20optimization%20and%20differential%20evolution-based.pdf Kishore, D. J. Krishna and Mohamed, M. R. and Sudhakar, K. and Peddakapu, K. (2022) Grey wolf optimization and differential evolution-based maximum power point tracking controller for photovoltaic systems under partial shading conditions. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 44 (3). 6286 -6302. ISSN 1556-7036 (Print); 1556-7230 (Online). (Published) https://doi.org/10.1080/15567036.2022.2096154 https://doi.org/10.1080/15567036.2022.2096154 |
| spellingShingle | TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Kishore, D. J. Krishna Mohamed, M. R. Sudhakar, K. Peddakapu, K. Grey wolf optimization and differential evolution-based maximum power point tracking controller for photovoltaic systems under partial shading conditions |
| title | Grey wolf optimization and differential evolution-based maximum power point tracking controller for photovoltaic systems under partial shading conditions |
| title_full | Grey wolf optimization and differential evolution-based maximum power point tracking controller for photovoltaic systems under partial shading conditions |
| title_fullStr | Grey wolf optimization and differential evolution-based maximum power point tracking controller for photovoltaic systems under partial shading conditions |
| title_full_unstemmed | Grey wolf optimization and differential evolution-based maximum power point tracking controller for photovoltaic systems under partial shading conditions |
| title_short | Grey wolf optimization and differential evolution-based maximum power point tracking controller for photovoltaic systems under partial shading conditions |
| title_sort | grey wolf optimization and differential evolution-based maximum power point tracking controller for photovoltaic systems under partial shading conditions |
| topic | TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/37278/ http://umpir.ump.edu.my/id/eprint/37278/ http://umpir.ump.edu.my/id/eprint/37278/ http://umpir.ump.edu.my/id/eprint/37278/1/Grey%20wolf%20optimization%20and%20differential%20evolution-based%20maximum%20power%20.pdf http://umpir.ump.edu.my/id/eprint/37278/2/Grey%20wolf%20optimization%20and%20differential%20evolution-based.pdf |