Degradation state estimation for insulated gate bipolar transistor based on multi-scale fusion learning

Abstract

Insulated gate bipolar transistor (IGBT) modules are widely used in power electronic systems. Estimating the degradation state of IGBT modules is especially crucial when operating under different conditions, as they are highly likely to experience aging or faults, resulting in system failure. However, a fixed time window is generally used, which is not easy to fully learn the correlation between data. To address the issues, this paper proposes a multi-scale fusion learning method for estimating the degradation state of IGBT. The method can effectively mine the short-term and long-term correlations of IGBT aging data, which helps to implement the accurate estimation based on IGBT aging data. To validate the effectiveness of the proposed method, the accelerated aging test data provided by the NASA Predictive Center of Excellence (PCoE) is utilized. The results show that the method can accurately estimate the degradation state of IGBTs.