In this article, a new indirect matrix converter (IMC) topology with two insulated gate bipolar transistors (IGBT) set in the upper and lower dc-link is proposed to suppress the common-mode voltage (CMV). Meanwhile, two new space vector modulation (SVM) strategies are redesigned to control this topology. The first strategy utilizes the equivalent zero voltage vectors to replace the zero voltage vectors for reducing the CMV, while the second strategy selects and utilizes specific active voltage vectors and equivalent zero voltage vectors to suppress the CMV based on the input current sector. Comparative simulation and experimental research for the two strategies are carried out for the new IMC topology, which validates that the CMV can be reduced to 57.7% or 37.1% of the one in the conventional IMC system. Besides, the proposed new IMC system is experimentally verified that it can keep a satisfied CMV suppression performance when feeding an induction motor. Moreover, the efficiency analysis also indicates that the new topology will not bring in obvious additional power loss under the proposed SVM strategies even though two IGBTs are added in the dc-link.

Aiming at the optimization of current stress with low voltage ratio and full ZVS, a control method combining variable duty cycle and phase shift was proposed based on dual active bridge (DAB) converters with DC blocking capacitors. By adding DC bias to the DC blocking capacitors, asymmetric duty modulation (ADM) can adjust the bias as needed. Based on the theoretical analysis of steady-state operation, the operating modes can be divided into eight modes. According to the features of each mode, equivalent circuits are established. The transmission power and the boundary of zero-voltage-switching (ZVS) are deduced through a detailed analysis of each mode. Based on the theoretical deduction, ADM is more suitable for a low voltage ratio. Verified by experiment, optimized asymmetric duty modulation (OADM) can increase efficiency by 3.58%, 6.57%, 8.81%, and 10.33% compared with DPS when P is equal to 0.36 and m is equal to 0.4, 0.3, 0.2, and 0.1, respectively. Using this method, the current stress of the converter is lighter than that under regular modulation when the voltage ratio m ≤ 0.5 with full ZVS.

SiC MOSFETs face prominent reliability issues due to higher voltage resistance requirements and continued device miniaturization. The lifetime prediction of SiC MOSFET plays a crucial role in improving the reliability of devices and systems. However, existing methods still face challenges in terms of adaptability, stability, and accuracy due to the complexity of the failure process in SiC MOSFET. This article proposes an improved grey wolf optimizer-based long short-term memory (IGWO-LSTM) model for SiC MOSFET lifetime prediction. The model introduces a Tent chaotic mapping to generate an initial population with optimal distribution, ensuring comprehensive search space coverage and enhancing dynamic search adaptability. Then, a nonlinear control parameter strategy and the principle of particle swarm optimization (PSO) are added. The feature extraction capability of the model is strengthened, and the exploration and exploitation phases are dynamically balanced. The optimizations enable faster discovery of the global optimum while maintaining solution quality, thereby improving prediction accuracy and stability. Finally, power cycling experiments were conducted on two types of SiC MOSFETs with different internal resistances to validate the effectiveness of the proposed model. The proposed IGWO-LSTM model achieves high prediction accuracy, with R² values of 96.2%, 94.8%, 94.1%, and 93.9% for four SiC MOSFETs, and RMSE values as low as 0.0117, 0.0143, 0.0152, and 0.0158, respectively. This represents an average improvement in R² by 16%, 8%, and 4%, and a reduction in RMSE by up to 67.03%, 50.39%, and 31.57% compared with other intelligent models. Similarly, IGWO-LSTM achieves reductions in MAE of approximately 68%, 50%, and 30%, with corresponding reductions in MAPE of about 70%, 48%, and 26%, respectively. The results demonstrate superior performance in prediction accuracy, stability, and adaptability of the proposed model.