Search

This paper proposes a control method for balancing the voltages of series-connected SiC MOSFETs based on active clamping and comparison circuitry. Each SiC MOSFET is paired with an active clamping module (ACM), which comprises an auxiliary switch and a capacitor. Equal voltage distribution across the SiC MOSFETs is achieved by maintaining balance among the voltages of the ACM capacitors. This voltage balance is achieved through a local comparison circuit with the threshold set at the blocking voltage of the SiC MOSFET in its OFF state. When the ACM capacitor voltage exceeds this threshold, the auxiliary switch is triggered on during the dead time, and the excess energy of this capacitor is recycled back to the DC bus. The proposed adaptive balancing process maintains all ACM capacitor voltages at the threshold without requiring system-level coordination or communication between ACMs, thus simplifying the control circuits. The method does not affect the SiC MOSFETs’ switching speed and enables zero-voltage switching on for the auxiliary switches. Comprehensive operating principles and key parameter designs are provided for both DC transformer (DCT) and Voltage Source Converter (VSC) applications. A high-voltage power module, incorporating six SiC MOSFETs in series, was constructed to demonstrate the effectiveness of the proposed approach. Tests on a DCT and a VSC incorporating this high-power module validated the efficacy of the proposed voltage balancing method.

Millimeter-wave (mmWave) synthetic aperture radar (SAR) has found wide applications in autonomous driving, landslide detection, urban mapping, etc. However, the high propagation loss of mmWave bands and the limitation of transmitting power have led to limited imaging performance for mmWave SAR. In this article, an enhanced SAR imaging framework that combines along-track multiple channels is proposed using a low-rank tensor-train (TT) decomposition method, which is applicable for a co-located multiple input multiple output (MIMO) array or a phased-transmitting-digital-receiving array. First, the multichannel images are stacked into a tensor form after SAR imaging on individual channels and spatial variant array phase correction for each pixel. Then, the low-rank property of tensor stack is exploited and the TT model is utilized to find the redundancy and leverage the intrinsic structure of image stack. In addition, ket augmentation is introduced to exhibit the local data structure more clearly than the original tensor under TT decomposition. Finally, tensor-train nuclear norm is used to relax the NP-hard problem with low-rank constraint and the minimization problem is solved in the framework of alternating direction method of multipliers for enhanced imaging. The proposed algorithm can effectively improve the working distance and image quality of mmWave SAR. Numerical experiments using simulated data of MIMO SAR and measured data collected by a ground-based phased-transmitting-digital-receiving array system are carried out to verify the performance of the proposed algorithm.