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.

中文翻译：

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用于高级交通运输系统实时仿真的机器学习构建块

人工智能（AI）的革命正在改变全球主要行业。通过精确的推理，AI吸引了许多工程师和科学家的注意力。有希望的是，经过全面研究后，硬件在环（HIL）仿真可以采用这种类型的建模方法作为替代方法之一。本文提出了一种在不使用任何传统的面向电路的瞬态求解器的情况下，使用机器学习构建模块（MLBB）来仿真高级运输应用（ATA）中的电力电子电机驱动器瞬态的方法。选择更多的电动飞机（MEA）电源系统作为案例研究，以验证MLBB的实时仿真性能。在MLBB内部，神经网络（NN）已用于为各种设备构建组件级，设备级和系统级模型。这些模型在集群中经过良好训练，并被移植到基于现场可编程门阵列（FPGA）的硬件平台中。最后，将MLBB仿真结果与PSCAD / EMTDC（系统级别）和SaberRD（设备级别）进行了比较，结果显示出模型一致性高，硬件执行速度高。