Foreign Object Detection for Wireless Power Transfer Based on Machine Learning

被引：0

作者：

Ote, Masaya ^{[1
]}

Jeong, Soyeon ^{[1
]}

Tentzeris, Manos M. ^{[1
]}

机构：

[1] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA

来源：

2020 IEEE WIRELESS POWER TRANSFER CONFERENCE (WPTC) | 2020年

关键词：

Wireless power transfer; WPT; magnetic resonance; foreign object detection; FOD; machine learning; neural network;

D O I：

10.1109/wptc48563.2020.9295641

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

A simple foreign object detection (FOD) method utilizing a neural network is discussed. For a receiver and transmitter structure, open helical types of coil resonating at 13.56 MHz were used with different distances and with and without the presence of foreign objects (a copper plate or plastic bottle filled with water). An FOD system was constructed based on a neural network that detects foreign objects based only on reflection coefficient (S11) parameters. This approach for FOD achieved an accuracy of approximately 98% in the range 12-18 cm.

引用

页码：476 / 479

页数：4

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