MPPT algorithm based on modified remora optimization algorithm for photovoltaic systems under partial shading conditions
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| Název: | MPPT algorithm based on modified remora optimization algorithm for photovoltaic systems under partial shading conditions |
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| Autoři: | Efendi, Moh. Zaenal, Adnaurrosyid, Akhmad, Habibi, Muhammad Nizar, Eviningsih, Rachma Prilian, Windarko, Novie Ayub, Jati, Mentari Putri |
| Přispěvatelé: | Politeknik Elektronika Negeri Surabaya (PENS), Power Electronics Laboratory |
| Zdroj: | Journal of Mechatronics, Electrical Power and Vehicular Technology; Vol 16, No 2 (2025) ; Journal of Mechatronics, Electrical Power, and Vehicular Technology; Vol 16, No 2 (2025) ; 2088-6985 ; 2087-3379 ; 10.55981/mev.v16i2 |
| Informace o vydavateli: | National Research and Innovation Agency |
| Rok vydání: | 2025 |
| Sbírka: | Journal of Mechatronics, Electrical Power, and Vehicular Technology (MEV) |
| Témata: | Electrical, Power, Energy, panel, partial shading, maximum power point tracking (MPPT), modified remora optimization algorithm (MROA) |
| Popis: | The increasing electricity demand, driven by the growing human population, has led to the need for efficient backup power sources. Solar panels are one of the renewable energy sources that have been widely developed because they only require solar energy as their primary source. However, the phenomenon of partial shading is often a problem in solar panels because it can reduce the output power of the solar panel system, which is caused by shadows from trees or clouds. In this condition, conventional maximum power point tracking (MPPT) algorithms are often limited to the local maximum power point (LMPP). To effectively attain the global maximum power point (GMPP), it is imperative to devise more efficient algorithms. The modified remora optimization algorithm (MROA) has been proposed as a potential solution to this challenge. MROA is an adaptation of the remora optimization algorithm (ROA), inspired by the behavior of remora fish. The results indicate that the algorithm achieves an average accuracy of approximately 99.13 % in both simulation and hardware implementations. Furthermore, when comparing the results of the MROA with those of the original ROA method and particle swarm optimization (PSO), the MROA exhibited superior accuracy, tracking time, and power gain, suggesting that the MROA algorithm effectively circumvents the limitation of the local maximum power point. |
| Druh dokumentu: | article in journal/newspaper |
| Popis souboru: | application/pdf |
| Jazyk: | English |
| Relation: | https://mev.brin.go.id/mev/article/view/1257/pdf; https://mev.brin.go.id/mev/article/view/1257 |
| DOI: | 10.55981/j.mev.2025.1257 |
| Dostupnost: | https://mev.brin.go.id/mev/article/view/1257 https://doi.org/10.55981/j.mev.2025.1257 |
| Rights: | Copyright (c) 2025 Moh. Zaenal Efendi, Akhmad Adnaurrosyid, Muhammad Nizar Habibi, Rachma Prilian Eviningsih, Novie Ayub Windarko, Mentari Putri Jati ; http://creativecommons.org/licenses/by-nc-sa/4.0 |
| Přístupové číslo: | edsbas.CBE7FC44 |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | The increasing electricity demand, driven by the growing human population, has led to the need for efficient backup power sources. Solar panels are one of the renewable energy sources that have been widely developed because they only require solar energy as their primary source. However, the phenomenon of partial shading is often a problem in solar panels because it can reduce the output power of the solar panel system, which is caused by shadows from trees or clouds. In this condition, conventional maximum power point tracking (MPPT) algorithms are often limited to the local maximum power point (LMPP). To effectively attain the global maximum power point (GMPP), it is imperative to devise more efficient algorithms. The modified remora optimization algorithm (MROA) has been proposed as a potential solution to this challenge. MROA is an adaptation of the remora optimization algorithm (ROA), inspired by the behavior of remora fish. The results indicate that the algorithm achieves an average accuracy of approximately 99.13 % in both simulation and hardware implementations. Furthermore, when comparing the results of the MROA with those of the original ROA method and particle swarm optimization (PSO), the MROA exhibited superior accuracy, tracking time, and power gain, suggesting that the MROA algorithm effectively circumvents the limitation of the local maximum power point. |
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| DOI: | 10.55981/j.mev.2025.1257 |