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.
Highlights
Increasing adoption of a renovated power electronic drive system has been witnessed in the advanced transportation application (ATA) of more electric aircraft (MEA) [1] [2], allelectric ship (AES) [3] [4], traction [5], etc
This section compares the results of the proposed machine learning building blocks (MLBBs) based emulation on field-programmable gate array (FPGA) with the traditional transient methods, which utilizes PSCAD/EMTDC for system-level simulation and SaberRD for device-level simulation
This paper proposed MLBB-based modeling method to emulate the transients of ATA with high accuracy and execution efficiency from component-level (50 ns time-step), device-level (50 ns time-step), and system-level (1 μs timestep) on the FPGA platform in real-time
Summary
Increasing adoption of a renovated power electronic drive system has been witnessed in the advanced transportation application (ATA) of more electric aircraft (MEA) [1] [2], allelectric ship (AES) [3] [4], traction [5], etc. The development of artificial intelligence (AI) and its application-specific integrated circuit (ASIC) [6] give a new possibility to represent the nextgeneration circuit solver These newly developed AI neural network (NN) models are forecasting methods based on nonlinear mathematical equivalent, which has been applied in the areas of face verification [8], image resolution processing [7], human action recognition [9], and natural language processing [10]-[12].
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