A supervised machine-learning method for optimizing the automatic transmission system of wind turbines

Q2 Materials Science

Engineering Solid Mechanics Pub Date : 2022-01-01 DOI:10.5267/j.esm.2021.11.001

Habeeb A. H. R. Aladwani, M. Ariffin, F. Mustapha

{"title":"A supervised machine-learning method for optimizing the automatic transmission system of wind turbines","authors":"Habeeb A. H. R. Aladwani, M. Ariffin, F. Mustapha","doi":"10.5267/j.esm.2021.11.001","DOIUrl":null,"url":null,"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.","PeriodicalId":37952,"journal":{"name":"Engineering Solid Mechanics","volume":"34 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Solid Mechanics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5267/j.esm.2021.11.001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Materials Science","Score":null,"Total":0}

引用次数: 1

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.

查看原文本刊更多论文

风力发电机自动传动系统优化的监督式机器学习方法

大型风力发电机组多采用无级变速(CVT)作为传动系统，具有较高的效率。然而，它的复杂性和成本也很高。相比之下，市场上现有的小型风力涡轮机仅在没有齿轮比变化的情况下提供单速齿轮传动系统，导致能量收集效率低并导致齿轮故障。在这项研究中，提出了一种无监督机器学习算法来解决垂直轴风力涡轮机(VAWT)自动传动系统的能源效率问题，以提高其收集能量的效率。目的是在考虑自动传动系统的情况下，找到VAWT的最佳调整。为此，在采用离心式离合器进行自动换挡的同时，对该系统进行了不同传动比条件下的仿真和测试。结果表明，自动传动系统能够成功地根据风速调节转速。结果表明，采用自动输电系统的VAWT获得的电压和功率水平得到了显著提高。因此，具有机器学习能力的自动vawt可以更有效地根据风速进行自我调整。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Engineering Solid Mechanics Materials Science-Metals and Alloys

CiteScore

3.00

自引率

0.00%

发文量

期刊介绍： Engineering Solid Mechanics (ESM) is an online international journal for publishing high quality peer reviewed papers in the field of theoretical and applied solid mechanics. The primary focus is to exchange ideas about investigating behavior and properties of engineering materials (such as metals, composites, ceramics, polymers, FGMs, rocks and concretes, asphalt mixtures, bio and nano materials) and their mechanical characterization (including strength and deformation behavior, fatigue and fracture, stress measurements, etc.) through experimental, theoretical and numerical research studies. Researchers and practitioners (from deferent areas such as mechanical and manufacturing, aerospace, railway, bio-mechanics, civil and mining, materials and metallurgy, oil, gas and petroleum industries, pipeline, marine and offshore sectors) are encouraged to submit their original, unpublished contributions.