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Engineering Solid Mechanics

ISSN 2291-8752 (Online) - ISSN 2291-8744 (Print)
Quarterly Publication
Volume 10 Issue 1 pp. 35-56 , 2022

A supervised machine-learning method for optimizing the automatic transmission system of wind turbines Pages 35-56 Right click to download the paper Download PDF

Authors: Habeeb A. H. R. Aladwani, Mohd Khairol Anuar Ariffin, Faizal Mustapha

DOI: 10.5267/j.esm.2021.11.001

Keywords: Wind Turbine, Automatic Transmission System, Machine-learning, Energy Loss, Python

Abstract: 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.

How to cite this paper
Aladwani, H., Ariffin, M & Mustapha, F. (2022). A supervised machine-learning method for optimizing the automatic transmission system of wind turbines.Engineering Solid Mechanics, 10(1), 35-56.

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Journal: Engineering Solid Mechanics | Year: 2022 | Volume: 10 | Issue: 1 | Views: 1372 | Reviews: 0

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