1. Machine Learning Building Blocks for Real-Time Emulation of Advanced Transport Power Systems
- Author
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Venkata Dinavahi, Songyang Zhang, and Tian Liang
- Subjects
silicon carbide (SiC) ,Computer science ,gated recurrent units (GRU) ,02 engineering and technology ,Machine learning ,computer.software_genre ,Artificial intelligence (AI) ,Electric power system ,Gate array ,0202 electrical engineering, electronic engineering, information engineering ,Transient (computer programming) ,hardware -in-the-loop (HIL) ,Field-programmable gate array ,Emulation ,Artificial neural network ,business.industry ,machine learning (ML) ,long short-term memory (LSTM) ,020208 electrical & electronic engineering ,recurrent neural network (RNN) ,Solver ,insulated -gate bipolar transistor (IGBT) ,TK1-9971 ,Recurrent neural network ,power electronics ,real-time systems ,field -programmable gate arrays (FPGAs) ,more electric aircraft (MEA) ,020201 artificial intelligence & image processing ,Artificial intelligence ,Electrical engineering. Electronics. Nuclear engineering ,business ,computer - Abstract
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.
- Published
- 2020