Skip to main content
Unpublished Paper
On Condition Monitoring of High Switching Frequency Power GaN Converters with Adaptive Prognostics.pdf
(2018)
  • Mehrdad Biglarbegian, University of North Carolina at Charlotte
Abstract
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.
Disciplines
Publication Date
March 8, 2018
Citation Information
Mehrdad Biglarbegian. "On Condition Monitoring of High Switching Frequency Power GaN Converters with Adaptive Prognostics.pdf" (2018)
Available at: http://works.bepress.com/mehrdad-biglarbegian/9/