Prediction Procedure of Parasitic Parameters Considering Laminated Bus Bar Geometries Based on Online Machine Learning

The development of SiC and GaN power devices to achieve high-speed switching operations in power converter circuits is underway. The stray inductance caused by the bus bar geometries between DC capacitors and power devices influences high-speed switching circuits, such as surge voltages and switching losses. Therefore, the evaluation of the parasitic parameters is essential in designing power converter circuits. Currently, parasitic parameters that consider various bus bar geometries are calculated using finite element analysis (FEA) each time, which requires a large calculation time. This article proposes a prediction procedure for the parasitic parameters that easily and quickly consider complex bus bar geometries by performing online machine learning of FEA-based datasets. A laminated bus bar with two apertures to connect DC capacitors or power modules is analyzed, and the parasitic resistance, inductance, and capacitance are predicted. This article describes how large datasets can be obtained from multiple installments and perform online machine learning using XGBoost. To discuss the benefits of online machine learning, the prediction accuracy using test data in multiple machine learning models with different amounts of training data is compared. The mean relative error (MRE) between the predicted and analyzed values improves from 250% to 8% when parasitic parameters are predicted in the frequency range of 50 kHz to 100 MHz.