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Optimized dv/dt, di/dt sensing for a digitally controlled slope shaping gate driver

  • In a digitally controlled slope shaping system, reliable detection of both voltage and current slope is required to enable a closed-loop control for various power switches independent of system parameters. In most state-of-the-art works, this is realized by monitoring the absolute voltage and current values. Better accuracy at lower DC power loss is achieved by sensing techniques for a reliable passive detection, which is achieved through avoiding DC paths from the high voltage network into the sensing network. Using a high-speed analog-to-digital converter, the whole waveform of the transient derivative can be stored digitally and prepared for a predictive cycle-by-cycle regulation, without requiring high-precision digital differentiation algorithms. To gain an accurate representation of the voltage and current derivative waveforms, system parasitics are investigated and classified in three sections: (1) component parasitics, which are identified by s-parameter measurements and extraction of equivalent circuit models, (2) PCB design issues related to the sensing circuit, and (3) interconnections between adjacent boards. The contribution of this paper is an optimized sensing network on the basis of the experimental study supporting fast transition slopes up to 100 V/ns and 1 A/ns and beyond, making the sensing technique attractive for slope shaping of fast switching devices like modern generation IGBTs, CoolMOSTM and SiC mosfets. Measurements of the optimized dv/dt and di/dt setups are demonstrated for a hard switched IGBT power stage.

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Metadaten
Author of HS ReutlingenGröger, Johannes; Schindler, Alexis; Wicht, Bernhard
DOI:https://doi.org/10.1109/APEC.2017.7931209
ISBN:978-1-5090-5366-7
Erschienen in:APEC 2017 : Thirty Second Annual IEEE Applied Power Electronics Conference and Exposition : March 26-30, 2017, Tampa, Florida
Publisher:IEEE
Place of publication:Piscataway, NJ
Document Type:Conference Proceeding
Language:English
Year of Publication:2017
Page Number:6
First Page:3564
Last Page:3569
DDC classes:620 Ingenieurwissenschaften und Maschinenbau
Open Access?:Nein
Licence (German):License Logo  Lizenzbedingungen IEEE