Machine Learning Based Modeling for Real-Time Inferencer-in-the-Loop Hardware Emulation of High-Speed Rail Microgrid
نویسندگان
چکیده
The application of artificial intelligence (AI) technology in the field power systems and electronic devices is increasingly prevalent. With massive datasets generated by a wide range equipment, AI-based modeling promising future hardware-in-the-loop emulation. This article studies improves machine learning based approach for devices, inferencer-in-the-loop (IIL) system proposed together with optimized neural network (NN) models. high-speed rail microgrid, includes autotransformer rectifier unit subsystems, energy storage two-level converter permanent magnet synchronous motor propulsion modular multilevel induction serves as study cases to demonstrate adaptability this approach. Finally, show high accuracy versatility IIL real-time emulation system, system-level (1 $\boldsymbol{\mu} \mathbf{s}$ timestep) device-level (50 notation="LaTeX">${\text{ns}}$ results are compared three domains: referencer (C code simulation program NVIDIA Jetson offline SaberRD datasets), inferencer on Xilinx VCU118 board, online refined board.
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ژورنال
عنوان ژورنال: IEEE journal of emerging and selected topics in industrial electronics
سال: 2022
ISSN: ['2687-9743', '2687-9735']
DOI: https://doi.org/10.1109/jestie.2022.3179959