Journal
IEEE OPEN JOURNAL OF POWER ELECTRONICS
Volume 1, Issue -, Pages 488-498Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/OJPEL.2020.3039117
Keywords
Mathematical model; Logic gates; Artificial neural networks; Recurrent neural networks; Power electronics; Hardware; Complexity theory; Artificial intelligence (AI); field -programmable gate arrays (FPGAs); gated recurrent units (GRU); hardware -in-the-loop (HIL); insulated -gate bipolar transistor (IGBT); long short-term memory (LSTM); machine learning (ML); more electric aircraft (MEA); power electronics; real-time systems; recurrent neural network (RNN); silicon carbide (SiC)
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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.
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