Parameters sensitivity analysis of silicon carbide buck converters to extract features for condition monitoring

This paper proposes a study on the possibility of using measurable electrical quantities in a DC/DC converter to infer the state of health of active and passive components. We worked out the dependence of several features of the output voltage waveform on the parametric drift of the main switch, the diode, the tank inductor, and the output capacitor. The goal is to use these findings for the implementation of machine-learning algorithms for indirect condition monitoring, i.e., not relying on the direct measurement of the critical parameters. The case study is a buck converter based on silicon-carbide MOSFETs. Simulation results show the sensitivity of various output voltage signal features to these parameters and also their correlations, and as a result, the most appropriate features for the condition monitoring purpose. The same approach can be implemented for other converters.