A Framework for Online Estimation of High Frequency Converter Circuit Parameters

ABSTRACT In this paper a parameter estimation method of high frequency switching power converters is proposed. Parameters are estimated through measurement of basic circuit voltage and current quantities and using simple feed forward neural networks to establish correlations between circuit parameter variations and general converter performance. This allows the estimation of internal semiconductor device or passive component parameters that would be challenging to measure directly. This approach serves as a promising enabler for power converter digital twins and for converter health monitoring. The proposed framework is developed and verified for an LLC resonant converter. Parameter predictions achieved mean absolute errors below 4.12% and an average MAE of 1.57% for all parameters.