Improving Fault Isolation in DC/DC Converters Based with Bayesian Belief Networks

摘要

This paper lies in the domain of Fault Detection and Isolation (FDI). A Bayesian Naive Classifier (BNC) structure is selected and used as a first attempt to use Bayesian Belief Networks (BBNs) for DC/DC power converter FDI. In order to highlight the BNC capabilities, it is compared to the well known and used FDI method based on Proportional Observer (PO). This comparative study is based on real data collected from a Zero Volt Switch (ZVS) Full Bridge Isolated Buck converter.