Convolutional neural network for subharmonics detection in Hardware-In-the-Loop

Abstract

Hardware-In-the-Loop (HIL) has become a popular technique for testing real-time complex systems.One of its possible applications is power converter modeling in order to test digital controllers.When a switching converter model is used, the output of the controller can be read incorrectly by the model due to the lack of synchronization of their clocks.Therefore, undesirable oscillations (subharmonics) can appear in the output due to the aliasing effects, which prevent the HIL model from its normal functionality.While there are various solutions present in academic and industrial research in order to address this arising problem, there is no automatic algorithm to detect it when a reference model is not available. In this paper, we implement a one-dimensional convolutional neural network which allows detecting the aliasing distortions.The 1D network was chosen due to the kind of the signal to be classified - an inductor current of the converter model, which is in effect a 1D time sequence.The proposed CNN architecture is shallow and consists of 27 layers only.Despite its simplicity, it shows remarkable performance of more than 99% for both validation and testing datasets, and for the post-training characteristics.Moreover, it makes the architecture potentially implementable in embedded systems, like a real-time HIL system, what can be a good objective for future research.