Searcharxiv⌕ Search

arXiv · 2609.23000

Analysis and Design of Desaturation Short-Circuit Protection for SiC MOSFET Under High Switching Voltage Transients

Abstract

Short-circuit protection is vital for ensuring the safe operation of high-power-density power converters using Silicon Carbide (SiC) MOSFETs. However, adapting conventional desaturation (DESAT) protection, originally designed for Silicon (Si) IGBTs, is challenging due to the significantly shorter shortcircuit withstand time of SiC devices, typically less than 3μs. Nevertheless, DESAT protection can be adapted for SiC MOSFETs through certain circuit modifications. This article provides a comprehensive analysis and design guidelines for the three architectural variants of DESAT protection circuits, specifically for adjusting the short-circuit detection time suitable for SiC MOSFETs. Further, this article establishes a quantitative framework to evaluate the noise induced into the DESAT protection circuits during turn-off dv/dt switching transients. From this analysis, a design methodology and safe operating boundary are derived for each DESAT configuration, guiding the systematic selection of component values to balance dv/dt noise margin and short-circuit detection speed for SiC MOSFETs. Experimental results are presented to validate the analysis.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ravi Teja Pogulaguntla, Arun Karuppaswamy B. 2026-09-19. Analysis and Design of Desaturation Short-Circuit Protection for SiC MOSFET Under High Switching Voltage Transients. https://arxiv.org/abs/2609.23000

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Overcoming Cognitive Distraction and Measurement Noise: Strategies for Humans and Engineering Systems

Cognitive distraction and measurement noise are two distinct factors that significantly impact the performance of humans and engineering systems. Cognitive distraction occurs when an individual's attention is diverted from a task, while measurement noise refers to the random variation that can occur in system measurements. Although humans and engineering systems employ different methods to overcome these obstacles, the ultimate goal is to achieve optimal performance. An intriguing question arises: what are the similarities and differences between using the term "noise" in engineering and cognitive psychology? Additionally, it is worthwhile to explore whether the human brain and engineering control systems use similar or different approaches to attenuate noise. While this article does not provide a definitive answer, it emphasizes the importance of addressing this question and encourages further investigation. Furthermore, assistive cyber-physical devices such as wearable sensory-substitution navigation aids are highlighted as quintessential human-in-the-loop systems where engineering noise attenuation and human cognitive distraction mitigation must converge.

eess.SY↗

A Fully Analog Implementation of Model Predictive Control with Application to Buck Converters

This paper proposes a novel approach to design analog electronic circuits that implement Model Predictive Control (MPC) policies for dynamical systems described by affine models. Effective approaches to define a reduced-complexity explicit MPC are combined and applied to realize an analog circuit comprising a limited set of low-latency, commercially available components. The practical feasibility and effectiveness of the proposed approach are demonstrated through its application in the design of a novel controller for DC-DC Buck converters. We formally analyze the stability of the resulting system and conduct extensive numerical simulations to demonstrate the control system's performance in rejecting line and load disturbances.

eess.SY↗

Graph Neural Ordinary Differential Equations for Power System Identification with Transfer Learning Applications

With the shift towards decentralized energy generation, the increasing complexity of power systems renders physics-based modeling challenging. At the same time the growing amount of available measurement data opens the door for obtaining data-driven models. A modern method to do so are neural ordinary differential equations (NODEs), offering a framework for continuous-time system identification. Recent extensions, so called graph NODEs impose a structural inductive bias that has the potential to improve generalization of the learned representations. In this work, we employ and extend them to develop message-passing graph NODEs (MPG-NODEs) for identification of coupled systems with heterogeneous node dynamics and heterogeneous edge couplings. This includes state-of-the-art machine learning architectures to infer latent representations of unmeasured states, local node and edge embeddings to account for heterogeneity as well as an autoregressive scheme to allow for piecewise constant control inputs. We apply MPG-NODEs to identify voltage and frequency dynamics of power systems and compare them to a monolithic NODE under identical measurement assumptions. Our case study indicates that while achieving similar performance, our proposed MPG-NODEs are more flexible with transfer learning options that allow to add or remove edges and nodes with little retraining.

eess.SY↗