1. Real-Time HIL Emulation of DRM With Machine Learning Accelerated WBG Device Models
- Author
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Songyang Zhang, Tian Liang, and Venkata Dinavahi
- Subjects
Artificial intelligence (AI) ,DC railway microgrid (DRM) ,field-programmable gate arrays (FPGAs) ,gallium nitride (GaN) ,gated recurrent units (GRU) ,hardware-in-the-loop (HIL) ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - 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.
- Published
- 2023
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