This article presents an offline deep learning approach focused to model and identify a 270- to 28-V dc-dc step-down converter used in on-board distribution systems of more electric aircrafts (MEAs). Manufacturers usually do not provide enough information of the converters. Thus, it is difficult to perform design and planning tasks and to check the behavior of the power distribution system without an accurate model. This work considers the converter as a black box and trains a wavelet convolutional neural network (WCNN) that is able to accurately reproduce the behavior of the dc-dc converter from a large set of experimental data. The methodology to design a WCNN based on the characteristics of the input and output signals of the converter is also described. The method is validated with the experimental data obtained from a setup that replicates the 28-V on-board distribution system of an aircraft. The results presented in this article show a high correlation between the measured and estimated data, robustness, and low computational burden. This article also compares the proposed approach against other techniques presented in the literature. It is possible to extend this method to other dc-dc converters, depending on their requirements.

中文翻译：

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基于小波卷积神经网络的DC-C变换器黑盒建模


本文介绍了一种离线深度学习方法，重点对多电动飞机 (MEA) 机载配电系统中使用的 270V 至 28V DC-DC 降压转换器进行建模和识别。制造商通常不提供足够的转换器信息。因此，如果没有准确的模型，就很难执行设计和规划任务并检查配电系统的行为。这项工作将转换器视为黑匣子，并训练小波卷积神经网络 (WCNN)，该网络能够从大量实验数据中准确再现 DC-DC 转换器的行为。还描述了根据转换器输入和输出信号的特性设计 WCNN 的方法。该方法通过复制飞机 28V 机载配电系统的设置获得的实验数据进行了验证。本文提出的结果显示了测量数据和估计数据之间的高度相关性、鲁棒性和低计算负担。本文还将所提出的方法与文献中提出的其他技术进行了比较。可以将此方法扩展到其他 DC-DC 转换器，具体取决于它们的要求。