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This paper introduces a deep learning-based framework for identifying hand-drawn schematics of power converter circuits and performing automated simulations. The framework employs cutting-edge computer vision-based object detection models, such as YOLOv8, to achieve a high mean average precision (mAP) of 96.7% to accurately identify components. Wire tracing and connectivity are achieved through a combined architecture built upon classical image processing techniques and deep learning approaches. Detailed information extracted from a hand-drawn circuit schematic is used to automatically create its netlist for automated simulation through the spice engine. The proposed framework is successfully tested on various nonisolated (buck, boost) and isolated (flyback, full-bridge) converters under both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) operations. In the comprehensive assessment of the entire framework, its efficacy is tested on 140 newly drawn circuit diagrams. The overall accuracy in the generation of netlists reaches a high value of 95.71%, utilizing the robust component detection capabilities of YOLOv8. Moreover, the framework enables the generation of both graphical representations and adjacency matrices for circuit diagrams. This output serves as a valuable dataset generator, contributing to the rapidly advancing domains of machine learning, including graph neural networks and geometric learning, particularly in the application space of power and energy systems. This framework can be further employed as an educational tool, and the ideas introduced can be developed to generate fully automated and efficient power converter designs for real-world applications. 

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. 

DC microgrids incorporate several converters for distributed energy resources connected to different passive and active loads. The complex interactions between the converters and components and their potential failures can significantly affect the grids' resilience and health; hence, they must be continually assessed and monitored. This paper presents a machine learning-assisted prognostic health monitoring (PHM) and diagnosis approach, enabling progressive interactions between the converters at multiple nodes to dynamically examine the grid's (or micro-grid's) health in real time. By measuring the resulting impedance at the power converters' terminals at various grid nodes, a neural network-based classifier helps detect the grid's health condition and identify the potential fault-prone zones, along with the type and location of the fault type in the grid topology. For a faulty grid, a Naive Bayes and a support vector machine (SVM)-based classifiers are used to locate and identify the faulty type, respectively. A separate neural network-based regression model predicts the source power delivered and the loads at different terminals in a healthy grid network. The proposed concepts are supported by detailed analysis and simulation results in a simple four-terminal DC microgrid topology and a standard IEEE 5 Bus system.