Machine Learning Based Modeling for Real-Time Inferencer-in-the-Loop Hardware Emulation of High-Speed Rail Microgrid

Abstract

The application of artificial intelligence (AI) technology in the field of power systems and power electronic devices is increasingly prevalent. With massive datasets generated by a wide range of equipment, AI-based modeling is promising in the future of hardware-in-the-loop emulation. This article studies and improves the machine learning based modeling approach for power electronic devices, and the inferencer-in-the-loop (IIL) system is proposed together with optimized neural network (NN) models. The high-speed rail microgrid, includes autotransformer rectifier unit subsystems, energy storage subsystems, two-level converter based permanent magnet synchronous motor propulsion subsystems, and modular multilevel converter based induction motor propulsion subsystems, serves as study cases to demonstrate the adaptability of this approach. Finally, to show high accuracy and versatility of the IIL real-time emulation system, the system-level (1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\mu} \mathbf{s}$</tex-math></inline-formula> timestep) and device-level (50 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\text{ns}}$</tex-math></inline-formula> timestep) results are compared in three domains: the referencer system (C code simulation program in NVIDIA Jetson and offline SaberRD datasets), offline inferencer emulation on Xilinx VCU118 board, and online refined inferencer emulation on Xilinx VCU118 board.