Intelligent Junction Temperature Estimation of an IGBT Using Machine Learning and $V_{ce}$ Measurement

The reliability of semiconductors in power convert-ers depends mainly on the junction temperature
$(T_{j})$
, and it is important to monitor
$T_{j}$
for reliable operation. This paper proposes a predictive model for the
$T_{j}$
estimation of an IGBT using advanced machine learning (ML) techniques, incorporating on-stage voltage drop
$(V_{c\mathrm{e}})$
, which is one of the temperature-sensitive electrical parameters (TSEPs). Datasets collected from experiments are refined through extrapolation methods, and then a more precise
$T$
, estimation is performed through ML, based on deep neural networks (DNNs). This approach is effective in han-dling problems caused by non-linearity, such as inflection points while estimating
$T_{j}$
. In addition, a novel ensemble learning model is proposed to enhance estimation accuracy, and comprehensive analysis and comparison of ML algorithms are provided.