On condition monitoring of high frequency power GaN converters with adaptive prognostics

There is no doubt that in the future, a need for higher switching frequency is inevitable to extract the full benefits of reliable Gallium Nitride (GaN) device characteristics. Along with the reliability enhancement for GaN-based power converters, it is essential to monitor a precursor signature ide...

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Bibliographic Details
Main Authors: Biglarbegian, Mehrdad, Mostafavi, Saman, Hauer, Sven, Nibir, Shahriar Jalal, Kim, Namwon, Cox, Robert, Parkhideh, Babak
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Failure analysis
Fatigue
Fault prognostics
Gallium nitride
high frequency dc-dc converter
internet of things
machine learning
metropolis-hasting
Monitoring
power GaN
Predictive models
Reliability
wide bandgap semiconductors
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Summary:There is no doubt that in the future, a need for higher switching frequency is inevitable to extract the full benefits of reliable Gallium Nitride (GaN) device characteristics. Along with the reliability enhancement for GaN-based power converters, it is essential to monitor a precursor signature identification for diagnostics/prognostics techniques. With the availability of the most granular information deduced from advanced devices, a new data-driven scheme is proposed for system monitoring and possible lifetime extension of 400W power GaN converters at 100kHz. The approach relies on the real-time R ds ( on ) data extraction from the power converter, and calibration of an adaptive model using multi-physics co-simulations under thermal cycling. More specifically, the focus is on deploying machine learning algorithms to exploit for the parameter estimation in power electronics engineering reliability.
ISSN:2470-6647
DOI:10.1109/APEC.2018.8341180