Sequence Prediction for SiC MOSFET Active Gate Driving With a Recurrent Neural Network
Author:
Affiliation:
1. FREEDM System Center, North Carolina State University, Raleigh, NC, USA
2. Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA
Funder
Texas Instruments
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Medicine
Link
http://xplorestaging.ieee.org/ielx7/8782707/10008994/10171405.pdf?arnumber=10171405
Reference33 articles.
1. Optimization Platform to Find a Switching Pattern of Digital Active Gate Drive for Reducing Both Switching Loss and Surge Voltage
2. Parasitic Inductance and Capacitance-Assisted Active Gate Driving Technique to Minimize Switching Loss of SiC MOSFET
3. C2M0025120D silicon carbide power MOSFET,0
4. Direct Approach of Simultaneously Eliminating EMI-Critical Oscillations and Decreasing Switching Losses for Wide Bandgap Power Semiconductors
5. Global Optimality in Neural Network Training
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1. Data-Driven SiC MOSFET Active Gate Driving for 380V/3A LVDC System Protection Employing Recurrent Deep Neural Network;Electric Power Components and Systems;2024-05-24
2. A Partial Active Gate Control for Improvement of a Trade-Off Relation Between Surge Voltage and Efficiency in a Three-Phase Inverter;IEEE Transactions on Industry Applications;2024-05
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