Data-Driven Digital Twins for Monitoring the Health and Performance of Converters

This work introduces a machine learning approach for developing Digital Twins (DTs) for DC-DC converters, focusing on in-situ implementation in real-world operational conditions. A system based on a boost converter has been developed in MATLAB Simulink. To mirror real-world scenarios, commercial datasheets along with a range of input parameters, health degradation elements, temperature influence, and random noises have been considered. The study employs Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) for predicting critical circuit responses of the boost converter, including inductor current, output voltage, and efficiency. Investigations show that MLP performs relatively poorly in the presence of noise. The CNN and RNN outperform the MLP under various noise levels, with the RNN exhibiting the best performance. This work advances DTs technology in power electronics, aiming to improve converter system optimization and enable predictive maintenance.