An Active Learning Framework for Reliability-Oriented Power Electronics Design

This article proposes an active learning framework to address a longstanding research question: how much data are needed for data-driven power electronics designs. The proposed method can automatically explore the optimal scenario using minimal data while achieving the desired accuracy. To demonstrate the proposed method, the reliability-oriented design (ROD) for a traction converter is used as a case study. While traditional methods achieving ROD necessitate extensive simulations and experiments, the proposed method uses surrogate models and active learning to achieve the desired accuracy with minimal computational requirements. The performance of the proposed method has been verified by experimental measurements. The computation cost is reduced by 71.6% compared to the traditional ROD method.