Machine Learning Building Blocks for Real-Time Emulation of Advanced Transport Power Systems

• Author(s) / Creator(s)

  • Songyang Zhang, Tian Liang, Venkata Dinavahi

• 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. InsideMLBBs, 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.

• Date created

  2020-01-01

• Subjects / Keywords

  • 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).

• Type of Item

  Article (Published)

• DOI

  https://doi.org/10.7939/r3-gsf4-ay20

• License

  Attribution-NonCommercial-NoDerivatives 4.0 International