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

• Author(s) / Creator(s)

  • Songyang Zhang, Tian Liang, Venkata Dinavahi

• 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 paper highlights the significance of ultrafast 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 1ns), 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/EMTD 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.

• Date created

  2023-07-20

• Subjects / Keywords

  • Artificial intelligence (AI), DC railway microgrid (DRM), field-programmable gate arrays (FPGAs), gallium nitride (GaN), gated recurrent units (GRU), hardware-in-theloop (HIL), machine learning (ML), power electronics, real-time systems, silicon carbide (SiC), wide-bandgap (WBG).

• Type of Item

  Article (Published)

• DOI

  https://doi.org/10.7939/r3-y7e2-nd81

• License

  Attribution-NonCommercial 4.0 International