New Perspectives in Artificial Intelligence-Based Object Detection for Wireless Power Transfer Systems

被引:0
作者
Intravaia, Matteo [1 ]
Lozito, Gabriele Maria [1 ]
Villagrasa, Eliseo [2 ]
Corti, Fabio [1 ]
Reatti, Alberto [1 ]
Trivino, Alicia [2 ]
机构
[1] Univ Florence, Dept Informat Engn, Florence, Italy
[2] Univ Malaga, Escuela Ingenierias Ind, Malaga, Spain
来源
2024 IEEE 22ND MEDITERRANEAN ELECTROTECHNICAL CONFERENCE, MELECON 2024 | 2024年
关键词
Foreign Object Detection; Machine Learning; Wireless Power Transfer; Support Vector Machine; Feed Forward Neural Network; Classification Trees; MAGNETIC HYSTERESIS; COIL;
D O I
10.1109/MELECON56669.2024.10608776
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper proposes a possible approach for object detection in Wireless Power Transfer (WPT) systems using artificial intelligence techniques. The main objective is to identify the presence of foreign objects between the primary and secondary windings by processing voltage and current measurements. To achieve this goal, different machine learning methods are proposed and compared. One of the essential features of the proposed method is to avoid the use of additional differential voltage coils, whose effectiveness is affected by the misalignment between primary and secondary coils. Therefore, this work proposes a theoretical approach for identifying foreign metallic objects without additional sensors, sensing coils or expensive equipment.
引用
收藏
页码:230 / 235
页数:6
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