IGBT Module DPT Efficiency Enhancement via Multimodal Fusion Networks and Graph Convolution Networks

The dynamic electrical characteristics of
insulated-gate bipolar transistor (IGBT) are of great significance in practical high-power electrical applications and
are usually evaluated through double pulse test (DPT).
However, DPTs of IGBTs under various working conditions
are time-consuming and laborious. Traditional estimation
methods are based on detailed physical parameters and
complex formula calculations, making deployment process
challenging. This article proposes a novel DPT efficiency
enhancement method based on graph convolution network
(GCN) and feature fusion technology, which can estimate
and supplement switching transient waveforms of all working conditions. Thereby, dynamic electrical characteristics
of the IGBT are obtained by estimated waveforms of DPT.
This method proposes a multimodal attention fusion network to capture and fuse the features of switching transient
waveforms between different positions thereby improving
the expressive power and performance of the model. Moreover, this method is novel in that it is the first to utilize GCN
to embed DPT data under multiple working conditions into
a graph structure, which can use the graph structure information to fuse the features of spatially correlated working
conditions data to obtain reliable estimation results. The
method has been verified to be effective and accurate on
real dataset collected on two batches of IGBTs.