Browsing by Author "Bayne, Stephen B. (TTU)"
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Item Evaluation of GaN HEMTs in H3TRB Reliability Testing(2022) Rodriguez, Jose A. (TTU); Tsoi, Tsz (TTU); Graves, David (TTU); Bayne, Stephen B. (TTU)Gallium Nitride (GaN) power devices can offer better switching performance and higher efficiency than Silicon Carbide (SiC) and Silicon (Si) devices in power electronics applications. GaN has extensively been incorporated in electric vehicle charging stations and power supplies, subjected to harsh environmental conditions. Many reliability studies evaluate GaN power devices through thermal stresses during current conduction or pulsing, with a few focusing on high blocking voltage and high humidity. This paper compares GaN-on-Si High-Electron-Mobility Transistors (HEMT) device characteristics under a High Humidity, High Temperature, Reverse Bias (H3TRB) Test. Twenty-one devices from three manufacturers were subjected to 85◦C and 85% relative humidity while blocking 80% of their voltage rating. Devices from two manufacturers utilize a cascade configuration with a silicon metal-oxide-semiconductor field-effect transistor (MOSFET), while the devices from the third manufacturer are lateral p-GaN HEMTs. Through characterization, three sample devices have exhibited degraded blocking voltage capability. The results of the H3TRB test and potential causes of the failure mode are discussed.Item Machine Learning Based Foreign Object Detection in Wireless Power Transfer Systems(2024) Graves, David Z. (TTU); Bilbao, Argenis V. (TTU); Bayne, Stephen B. (TTU)As 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.