An efficient semi-mathematical model for co-pack IGBT

Chen, Na; Deng, Yan; Wu, Jiande; He, Xiangning

doi:10.1109/apec.2011.5744845

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…PoF-based prognostic methods rely on extensive knowledge of IGBT chemistry and failure mechanisms. Since the methods involve excessive physical parameters, the models are usually difficult to build precisely [ 8 ]. In contrast, the data-driven approaches require prognostic data that reflect the IGBT degradation behavior derived from ordinarily observed operating parameters such as voltage, current, power, and temperature [ 9 , 10 ] without the need of extensive knowledge on the devices.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Estimation of Insulated Gate Biploar Transistors (IGBTs) Based on a Novel Volterra k-Nearest Neighbor Optimally Pruned Extreme Learning Machine (VKOPP) Model Using Degradation Data

Liu

Mei

Zeng

et al. 2017

Sensors

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The insulated gate bipolar transistor (IGBT) is a kind of excellent performance switching device used widely in power electronic systems. How to estimate the remaining useful life (RUL) of an IGBT to ensure the safety and reliability of the power electronics system is currently a challenging issue in the field of IGBT reliability. The aim of this paper is to develop a prognostic technique for estimating IGBTs’ RUL. There is a need for an efficient prognostic algorithm that is able to support in-situ decision-making. In this paper, a novel prediction model with a complete structure based on optimally pruned extreme learning machine (OPELM) and Volterra series is proposed to track the IGBT’s degradation trace and estimate its RUL; we refer to this model as Volterra k-nearest neighbor OPELM prediction (VKOPP) model. This model uses the minimum entropy rate method and Volterra series to reconstruct phase space for IGBTs’ ageing samples, and a new weight update algorithm, which can effectively reduce the influence of the outliers and noises, is utilized to establish the VKOPP network; then a combination of the k-nearest neighbor method (KNN) and least squares estimation (LSE) method is used to calculate the output weights of OPELM and predict the RUL of the IGBT. The prognostic results show that the proposed approach can predict the RUL of IGBT modules with small error and achieve higher prediction precision and lower time cost than some classic prediction approaches.

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Section: Introductionmentioning

confidence: 99%