Zhang, Xiaotian, Hu, Yihua, Zhang, Jingwei, Esfahani, Mohammad Nasr, Tilford, Tim ORCID: https://orcid.org/0000-0001-8307-6403 and Stoyanov, Stoyan ORCID: https://orcid.org/0000-0001-6091-1226 (2024) IGBT module DPT efficiency enhancement via multimodal fusion networks and graph convolution networks. IEEE Transactions on Industrial Electronics. ISSN 0278-0046 (Print), 1557-9948 (Online) (doi:10.1109/TIE.2024.3368165)

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Abstract

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 is time-consuming and laborious. Traditional estimation methods are based on detailed physical parameters and complex formula calculations, making deployment process challenging. This paper 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 (MAFN) 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 utilise 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.

Item Type:	Article
Uncontrolled Keywords:	power electronics; reliability; numerical analysis
Subjects:	Q Science > Q Science (General) T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	17 Sep 2024 11:36
URI:	http://gala.gre.ac.uk/id/eprint/45910

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