Neural Network Based Parameter Identification for LLC Resonant Converters

This paper introduces a neural network based approach for parameter identification of a half-bridge LLC Resonant Converter. By using a set of measured inputs, the system can characterize the component values, and difficult to measure switch characteristics including the junction temperature or gate-source capacitance. This capability is useful for reliability, health monitoring and model based control applications. The range of the Mean Absolute Error (MAE) obtained for all parameters predictions was from 0.142% to 4.12%, with an average MAE of 1.57% over all parameters.