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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.

For being the world’s largest low voltage direct current (LVDC) microgrid (MG) in space, the power generation and distribution systems aboard the International Space Station (ISS) employ a hierarchical assortment of electric power sources, energy storage, control devices, power electronics, and loads operating cooperatively at multifarious system dispositions and multi-stage configurations. At the early phase of design, for such time-critical systems, the trade-off between reliability and convergence rate of device modeling, varying accuracy requirements of control flows, and especially the implementation for real-time performance have brought new challenges and problems for testing and validation of the MG. One of the solutions presented by this paper is to use the hardware-in-the-loop (HIL) emulation, where the MG is emulated using the field-programmable gate array (FPGA) hardware platform. In parallel with the emulation effort, comprehensive modeling solutions for both large-scale photovoltaic (PV) solar array wings (SAWs) and nonlinear behavior model (NBM) of insulated-gate bipolar transistors (IGBTs) have been utilized based on machine learning (ML) concepts of artificial neural network (ANN) and recurrent neural network (RNN). Both system-level (validated by Matlab/Simulink) and device-level (validated by SaberRD) transient simulations are carried out, and the results exhibit high accuracy and fidelity of the models and significant improvements in execution speed and hardware resource consumption.

The revolution of artificial intelligence (AI) is transforming major industries worldwide. With accurate inferencing, AI has caught the attention of many engineers and scientists. Promisingly, hardware-in-the-loop (HIL) emulation can adopt this type of modeling method as one of the alternatives after comprehensive investigation. This paper proposes an approach for emulating power electronic motor drive transients for advanced transportation applications (ATAs) using machine learning building blocks (MLBBs) without any traditional circuit-oriented transient solver. The more electric aircraft (MEA) power system is chosen as a case study to validate the real-time emulation performance of MLBBs. Inside MLBBs, neural networks (NNs) have been applied to build component-level, device-level, and system-level models for various equipment. These models are well trained in a cluster and transplanted into the field-programmable gate array (FPGA) based hardware platform. Finally, MLBB emulation results are compared with PSCAD/EMTDC for system-level and SaberRD for device-level, which showed high consistency for model accuracy and high speed-up for hardware execution.