Open AccessA supervised machine-learning method for optimizing the automatic transmission system of wind turbines
Author(s) -
Habeeb A. H. R. Aladwani,
Mohd Khairol Anuar Mohd Ariffin,
Faizal Mustapha
Publication year - 2022
Publication title -
engineering solid mechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.528
H-Index - 17
eISSN - 2291-8752
pISSN - 2291-8744
DOI - 10.5267/j.esm.2021.11.001
Subject(s) - wind power , clutch , transmission (telecommunications) , transmission system , computer science , continuously variable transmission , automotive engineering , automatic transmission , wind speed , energy (signal processing) , engineering , electrical engineering , telecommunications , statistics , physics , mathematics , meteorology
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.