Machine Learning Based Foreign Object Detection in Wireless Power Transfer Systems

dc.creatorGraves, David Z. (TTU)
dc.creatorBilbao, Argenis V. (TTU)
dc.creatorBayne, Stephen B. (TTU)
dc.date.accessioned2024-01-17T16:07:58Z
dc.date.available2024-01-17T16:07:58Z
dc.date.issued2024
dc.description© 2023 The Author(s) cc-by-nc-nd
dc.description.abstractAs power electronics continue to drive advancement in electronic devices by managing the system's energy flow, it is advantageous to examine all areas of energy transfer. Wireless power transfer is one section seeing increased adoption in consumer, industrial, and healthcare applications. This consequently increases the concern for safety, reliability, and efficiency. Power transmission to any unintended load is a primary safety issue in high-power wireless power transfer applications, such as unmanned aircraft systems. Foreign object detection is utilized to selectively transmit power to only the intended target to mitigate this issue. This paper seeks to demonstrate a novel alternative method of using machine learning and artificial intelligence techniques for performing foreign object detection solely on the transmitter portion of the system by only monitoring the TX coil current. This method is first evaluated in SPICE simulation software. Then by transitioning to a closed-loop hardware-based testbed designed to be deployed in unmanned aircraft systems. During the test process for the novel FOD method, the aggregate detection accuracy achieved was 83%, including worst-case misalignment conditions with the RX coil. When the outlying worst-case conditions are excluded, the detection accuracy improves to 97%. To measure the FOD effectiveness in preventing temperature rise in a metallic object, the metallic object was placed at the peak power draw location on the transmitter coil. When the metallic object is left at this location for extended periods, the novel FOD method lowers the maximum temperature reached by 86.8°C when compared to no FOD being active. The primary advantage to this novel method is the simplistic hardware additions, which only require minimal alterations to a WPTS without any FOD implemented. Along with this, implementing this foreign object detection method in the transmitter would mitigate the need for a direct communication link between the receiver and transmitter. As a result, the entire system complexity would decrease, thus increasing the UAS power density while maintaining a safe, effective, and reliable wireless power transmission method.
dc.identifier.citationGraves, D.Z., Bilbao, A.V., & Bayne, S.B.. 2024. Machine Learning Based Foreign Object Detection in Wireless Power Transfer Systems. e-Prime - Advances in Electrical Engineering, Electronics and Energy, 7. https://doi.org/10.1016/j.prime.2023.100384
dc.identifier.urihttps://doi.org/10.1016/j.prime.2023.100384
dc.identifier.urihttps://hdl.handle.net/2346/97469
dc.language.isoeng
dc.subjectForeign Object Detection
dc.subjectLong-Short-Term Memory
dc.subjectMachine Learning
dc.subjectPower electronics
dc.subjectWireless Power Transmission
dc.titleMachine Learning Based Foreign Object Detection in Wireless Power Transfer Systems
dc.typeArticle

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