A supervised machine-learning method for optimizing the automatic transmission system of wind turbines
Large-scale wind turbines mostly use Continuously Variable Transmission (CVT) as the transmission system, which is highly efficient. However, it comes with high complexity and cost too. In contrast, the small-scale wind turbines that are available in the market offer a one-speed gearing system only...
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| Format: | Article |
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Growing Science
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/100448/ |
| _version_ | 1848863326914740224 |
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| author | Aladwani, Habeeb A. H. R. Ariffin, Mohd Khairol Anuar Mustapha, Faizal |
| author_facet | Aladwani, Habeeb A. H. R. Ariffin, Mohd Khairol Anuar Mustapha, Faizal |
| author_sort | Aladwani, Habeeb A. H. R. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Large-scale wind turbines mostly use Continuously Variable Transmission (CVT) as the transmission system, which is highly efficient. However, it comes with high complexity and cost too. In contrast, the small-scale wind turbines that are available in the market offer a one-speed gearing system only where no gear ratios are varied, resulting in low efficiency of harvesting energy and leading to gears failure. In this research, an unsupervised machine-learning algorithm is proposed to address the energy efficiency of the automatic transmission system in vertical axis wind turbines (VAWT), to increase its efficiency in harvesting energy. The aim is to find the best adjustment for VAWT while the automatic transmission system is taken into account. For this purpose, the system is simulated and tested under various gear ratios conditions while a centrifugal clutch is applied to automatic gear shifting. The outcomes indicated that the automatic transmission system could successfully adjust the spinning in line with the wind speed. As a result, the obtained level of harvested voltage and power by VAWT with the automatic transmission system are improved significantly. Consequently, it is concluded that automatic VAWTs, equipped with the machine-learning capability can readjust themselves with the wind speed more efficiently. |
| first_indexed | 2025-11-15T13:31:09Z |
| format | Article |
| id | upm-100448 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:31:09Z |
| publishDate | 2022 |
| publisher | Growing Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1004482023-12-14T04:06:10Z http://psasir.upm.edu.my/id/eprint/100448/ A supervised machine-learning method for optimizing the automatic transmission system of wind turbines Aladwani, Habeeb A. H. R. Ariffin, Mohd Khairol Anuar Mustapha, Faizal Large-scale wind turbines mostly use Continuously Variable Transmission (CVT) as the transmission system, which is highly efficient. However, it comes with high complexity and cost too. In contrast, the small-scale wind turbines that are available in the market offer a one-speed gearing system only where no gear ratios are varied, resulting in low efficiency of harvesting energy and leading to gears failure. In this research, an unsupervised machine-learning algorithm is proposed to address the energy efficiency of the automatic transmission system in vertical axis wind turbines (VAWT), to increase its efficiency in harvesting energy. The aim is to find the best adjustment for VAWT while the automatic transmission system is taken into account. For this purpose, the system is simulated and tested under various gear ratios conditions while a centrifugal clutch is applied to automatic gear shifting. The outcomes indicated that the automatic transmission system could successfully adjust the spinning in line with the wind speed. As a result, the obtained level of harvested voltage and power by VAWT with the automatic transmission system are improved significantly. Consequently, it is concluded that automatic VAWTs, equipped with the machine-learning capability can readjust themselves with the wind speed more efficiently. Growing Science 2022 Article PeerReviewed Aladwani, Habeeb A. H. R. and Ariffin, Mohd Khairol Anuar and Mustapha, Faizal (2022) A supervised machine-learning method for optimizing the automatic transmission system of wind turbines. Engineering Solid Mechanics, 10 (1). 35 - 56. ISSN 2291-8744; ESSN: 2291-8752 https://growingscience.com/beta/esm/5187-a-supervised-machine-learning-method-for-optimizing-the-automatic-transmission-system-of-wind-turbines.html 10.5267/j.esm.2021.11.001 |
| spellingShingle | Aladwani, Habeeb A. H. R. Ariffin, Mohd Khairol Anuar Mustapha, Faizal A supervised machine-learning method for optimizing the automatic transmission system of wind turbines |
| title | A supervised machine-learning method for optimizing the automatic transmission system of wind turbines |
| title_full | A supervised machine-learning method for optimizing the automatic transmission system of wind turbines |
| title_fullStr | A supervised machine-learning method for optimizing the automatic transmission system of wind turbines |
| title_full_unstemmed | A supervised machine-learning method for optimizing the automatic transmission system of wind turbines |
| title_short | A supervised machine-learning method for optimizing the automatic transmission system of wind turbines |
| title_sort | supervised machine-learning method for optimizing the automatic transmission system of wind turbines |
| url | http://psasir.upm.edu.my/id/eprint/100448/ http://psasir.upm.edu.my/id/eprint/100448/ http://psasir.upm.edu.my/id/eprint/100448/ |