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GPU-based Multivariate IGBT Lifetime Prediction

Research Authors
Md Moniruzzaman, Ahmed H Okilly, Seungdeog Choi, Jeihoon Baek
Research Member
Research Department
Research Date
Research Year
2023
Research Journal
2023 IEEE Energy Conversion Congress and Exposition (ECCE)
Research Publisher
IEEE
Research_Pages
10.1109/ECCE53617.2023.10362123
Research Abstract

In the context of critical energy infrastructures (e.g., hydrogen infrastructure) that extensively utilize power converters, the need for reliable and accurate monitoring is of paramount importance. Addressing this necessity, this paper presents a novel GPU-based multivariate approach to Insulated Gate Bipolar Transistor (IGBT) lifetime prediction. Despite the substantial technological advances in the field, accurately predicting the lifetime of IGBTs remains a significant challenge. Current methods often rely on single precursor variable models, which can lack the precision required in demanding power electronic applications. In contrast, this study utilizes multiple precursor variables (V CE(ON) and case temperature) to achieve more accurate results. Initial results using NASA's open-source dataset, and Gaussian Process Regression (GPR) reveal that our multivariate model outperforms its single-variable counterparts in …

Research Rank
International Confrences