Machine Learning Approach of Optimal Frequency Tuning for Capacitive Wireless Power Transfer System

Wireless power transmission has become a remarkable research topic due to its enormous application potential. Recent advances in machine learning have been shown to be the most promising approach that offers significant capabilities in the wireless power transfer system (WPTS) for selecting the opti...

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Published in:International Journal of Emerging Technology and Advanced Engineering
Main Author: Hasan K.K.; Hairuddin M.A.; Mustapa R.F.; Nordin S.A.; Ashar N.D.K.
Format: Article
Language:English
Published: IJETAE Publication House 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143287258&doi=10.46338%2fijetae1122_07&partnerID=40&md5=a6cdb69312b83e311ed500d1ceafebd9
id 2-s2.0-85143287258
spelling 2-s2.0-85143287258
Hasan K.K.; Hairuddin M.A.; Mustapa R.F.; Nordin S.A.; Ashar N.D.K.
Machine Learning Approach of Optimal Frequency Tuning for Capacitive Wireless Power Transfer System
2022
International Journal of Emerging Technology and Advanced Engineering
12
11
10.46338/ijetae1122_07
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143287258&doi=10.46338%2fijetae1122_07&partnerID=40&md5=a6cdb69312b83e311ed500d1ceafebd9
Wireless power transmission has become a remarkable research topic due to its enormous application potential. Recent advances in machine learning have been shown to be the most promising approach that offers significant capabilities in the wireless power transfer system (WPTS) for selecting the optimal frequency tuning to achieve high efficiency performance. However, developing an automated frequency-tuned system remains a challenge. In this study, a novel frequency-tuned method is presented that utilises machine learning-based models such as neural networks (NN), support vector regression (SVR), and linear regression (LR) to estimate the best efficiency provided by the frequency level at the most optimal frequency tuning level from the experimental dataset, which is capable of aiding in the selection of the most efficient WPTS design. The results show that the SVR has the highest degree of accuracy, making it a promising option for optimising the tuning of power transfer systems while enhancing their performance efficiency. © 2022 by the Author(s).
IJETAE Publication House
22502459
English
Article
All Open Access; Bronze Open Access
author Hasan K.K.; Hairuddin M.A.; Mustapa R.F.; Nordin S.A.; Ashar N.D.K.
spellingShingle Hasan K.K.; Hairuddin M.A.; Mustapa R.F.; Nordin S.A.; Ashar N.D.K.
Machine Learning Approach of Optimal Frequency Tuning for Capacitive Wireless Power Transfer System
author_facet Hasan K.K.; Hairuddin M.A.; Mustapa R.F.; Nordin S.A.; Ashar N.D.K.
author_sort Hasan K.K.; Hairuddin M.A.; Mustapa R.F.; Nordin S.A.; Ashar N.D.K.
title Machine Learning Approach of Optimal Frequency Tuning for Capacitive Wireless Power Transfer System
title_short Machine Learning Approach of Optimal Frequency Tuning for Capacitive Wireless Power Transfer System
title_full Machine Learning Approach of Optimal Frequency Tuning for Capacitive Wireless Power Transfer System
title_fullStr Machine Learning Approach of Optimal Frequency Tuning for Capacitive Wireless Power Transfer System
title_full_unstemmed Machine Learning Approach of Optimal Frequency Tuning for Capacitive Wireless Power Transfer System
title_sort Machine Learning Approach of Optimal Frequency Tuning for Capacitive Wireless Power Transfer System
publishDate 2022
container_title International Journal of Emerging Technology and Advanced Engineering
container_volume 12
container_issue 11
doi_str_mv 10.46338/ijetae1122_07
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143287258&doi=10.46338%2fijetae1122_07&partnerID=40&md5=a6cdb69312b83e311ed500d1ceafebd9
description Wireless power transmission has become a remarkable research topic due to its enormous application potential. Recent advances in machine learning have been shown to be the most promising approach that offers significant capabilities in the wireless power transfer system (WPTS) for selecting the optimal frequency tuning to achieve high efficiency performance. However, developing an automated frequency-tuned system remains a challenge. In this study, a novel frequency-tuned method is presented that utilises machine learning-based models such as neural networks (NN), support vector regression (SVR), and linear regression (LR) to estimate the best efficiency provided by the frequency level at the most optimal frequency tuning level from the experimental dataset, which is capable of aiding in the selection of the most efficient WPTS design. The results show that the SVR has the highest degree of accuracy, making it a promising option for optimising the tuning of power transfer systems while enhancing their performance efficiency. © 2022 by the Author(s).
publisher IJETAE Publication House
issn 22502459
language English
format Article
accesstype All Open Access; Bronze Open Access
record_format scopus
collection Scopus
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