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

• Main Authors: Songyang Zhang, Tian Liang, Venkata Dinavahi
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Open Journal of Power Electronics
• Subjects: 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)
• Online Access: https://ieeexplore.ieee.org/document/9263346/

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.