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Hu, Yujie,Li, Zixin,Zhao, Cong,Li, Yaohua The Korean Institute of Power Electronics 2021 JOURNAL OF POWER ELECTRONICS Vol.21 No.11
A MOSFET-based series-resonant indirect-matrix-type power electronic transformer (PET) consists of line-frequency-switching folding-unfolding bridges and series-resonant dc-dc converters (SRCs), which is an attractive choice to achieve ac-ac conversion due to its high efficiency and high power density. However, the conduction losses, switching losses, and core losses in each of the high-frequency switching cycles T<sub>s</sub> are different due to the pulsating dc voltage |ac|, which introduces difficulties in the calculation of losses. In addition, the small dc capacitors also participate in the resonance, and the resonant current shape deviates from a pure sine. Therefore, the conventional loss model is not suitable for PETs. In this paper, the time-domain analysis of SRC resonant current considering the dc capacitor is developed. Then, the analytical expression of the resonant current RMS value in grid cycle T<sub>g</sub> is derived, which is more accurate in calculating conduction loss than the conventional model. In addition, it avoids the calculating losses in each T<sub>s</sub>. Next, an analytical switching loss model is developed based on the curve fitting of the capacitance-voltage relationship. A simplified analytical core loss model is provided. Each part of the loss of the PET is verified by thermal simulations, and the total losses are verified by efficiency tests on an experiment prototype.
Model averaging procedure for varying-coefficient partially linear models with missing responses
Jie Zeng,Weihu Cheng,Guozhi Hu,Yaohua Rong 한국통계학회 2018 Journal of the Korean Statistical Society Vol.47 No.3
This paper is concerned with model averaging procedure for varying-coefficient partially linear models with missing responses. The profile least-squares estimation process and inverse probability weighted method are employed to estimate regression coefficients of the partially restricted models, in which the propensity score is estimated by the covariate balancing propensity score method. The estimators of the linear parameters are shown to be asymptotically normal. Then we develop the focused information criterion, formulate the frequentist model averaging estimators and construct the corresponding confidence intervals. Some simulation studies are conducted to examine the finite sample performance of the proposed methods. We find that the covariate balancing propensity score improves the performance of the inverse probability weighted estimator. We also demonstrate the superiority of the proposed model averaging estimators over those of existing strategies in terms of mean squared error and coverage probability. Finally, our approach is further applied to a real data example.