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Real-Time HIL Emulation of DRM With Machine Learning Accelerated WBG Device Models

Authors :
Songyang Zhang
Tian Liang
Venkata Dinavahi
Source :
IEEE Open Journal of Power Electronics, Vol 4, Pp 567-578 (2023)
Publication Year :
2023
Publisher :
IEEE, 2023.

Abstract

The proliferation of artificial intelligence (AI) has opened up new avenues for the modeling of power electronics with ultra-fast transient responses, such as wide-bandgap (WBG) devices. This article highlights the significance of ultra-fast transient device-level hardware emulation for the DC railway microgrid (DRM) in real-time. To this end, the proposed approach partitions the DRM power system by transmission line method (TLM) and employs gated recurrent unit (GRU) and electromagnetic transient (EMT) modeling techniques for system-level subsystems. Meanwhile, for WBG devices, gallium nitride (GaN) high electron mobility transistors (HEMT) and silicon carbide (SiC) insulated gate bipolar transistors (IGBT) are modeled using a novel physical feature neuron network (PFNN), which offers high flexibility with a variable time-step (as low as 1 ns), thereby improving the accuracy, efficiency and accelerating the emulation on the field-programmable gate array (FPGA). The effectiveness of the proposed approach is confirmed by comparing the emulation results with offline simulation results obtained from PSCAD/EMTDC for system-level and SaberRD for device-level transients. The proposed PFNN approach provides strong versatility, ultra-fast transient emulation capability, and significantly improved accuracy, which bodes well for the future of power electronics device-level emulation.

Details

Language :
English
ISSN :
26441314
Volume :
4
Database :
Directory of Open Access Journals
Journal :
IEEE Open Journal of Power Electronics
Publication Type :
Academic Journal
Accession number :
edsdoj.04f2cf31916049c693b686638d6e28ca
Document Type :
article
Full Text :
https://doi.org/10.1109/OJPEL.2023.3297449