Compensated Neural Network Training Algorithm with Minimized Training Dataset for Modeling the Switching Transients of SiC MOSFETs

Ruwen Wang, Yu Chen (), Siyu Tong, Congzhi Cheng and Yong Kang
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Ruwen Wang: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Yu Chen: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Siyu Tong: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Congzhi Cheng: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Yong Kang: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2024, vol. 17, issue 23, 1-19

Abstract: Accurate modeling of the switching transients of SiC MOSFETs is essential for overvoltage evaluation, EMI prediction, and other critical applications. Due to the fast switching speed, the switching transients of SiC MOSFETs are highly sensitive to parasitic parameters and nonlinear components, making precise modeling challenging. This paper proposes a hybrid model for SiC MOSFET, in which the analytical model is treated as the basis to provide the fundamental waveforms (knowledge-driven), while the neural network (NN) is utilized to fit the high-order and nonlinear features (data-driven). An NN training method with augmented data is proposed to minimize the training datasets. Verification results show that, even though the NN is trained with the data from a single operating condition, the model can accurately predict switching transients of other operating conditions. The proposed methodology has the potential to co-work with the “black-box” or “grey-box” models to enhance the model accuracy.

Keywords: SiC MOSFET; analytical model; hybrid model; neural network; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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