A Deep Learning-Based Modeling of a 270 V-to-28 V DC-DC Converter Used in More Electric Aircrafts

Abstract

This article presents a novel approach for black-box modeling of 270 V-to-28 V dc–dc step-down converters used in more electric aircrafts. These converters normally feed constant power loads. The proposed deep learning approach, uses offline experimental data of the converter to find an accurate model that reproduces its behavior. It covers a broad range of loading conditions to build a model that replicates the whole behavior of the converter. This article compares the performance of the proposed method, which requires a very low computational burden once the model is trained, with that of a conventional recurrent neural network topology. Results presented in this article show the ability of the obtained solution to accurately emulate the behavior of the real step-down converter when the internal structure is unknown, with no knowledge of the internal parameters, thus preventing disclosure of manufacturer's confidential data. The modeling strategy presented in this article is validated with experimental data by using a step-down converter used in aircrafts. The approach is compared to existing modeling techniques to test its accuracy. This approach can also be applied to many power devices, including diverse types of power converters, power supplies, or filters among others.