The cell-based class of Voltage Source Converter (VSC) topologies, including Modular Multilevel Converter (MMC), are being considerably employed through the power system to interconnect heterogeneous electrical systems. Their versatility to play with the converter topology is widely exploited to accommodate several terminals and ports according with the specific application. One of the most challenging, and fundamental, question to comprehend the behavior of each topology concerns the definition of the so-called degrees of freedom (DOFs). The topology-geometry of the converter circuit is strongly characterizing its intrinsic DOFs; however, it is not yet considered a methodology to assess them in a generalized approach. For instance, the well-known Clarke transformation, to achieve the electrical DOFs, is only valid for a star-based system topology. Therefore, topology changes might lead to different, and not evident, converter DOFs. With respect to this, the main purpose of the article is to present a systematic methodology, scalable to every cell-based converter topology, to assess their electrical DOFs and the inherent ports affinities. The method lies in the topology-graph Laplacian spectral analysis, which indicates the structural normal modes at the converter points of connections. The analysis is then validated experimentally.

This study presents a novel approach for diagnosing machine conditions using deep learning models applied to Gabor-transformed motor current signatures. The dataset comprised signals from five single-phase 2-HP induction motors with five fault conditions, each tested under five different loading conditions. These signals were transformed into 2D time-frequency plots using the Gabor Transform (GT), capturing essential frequency changes over time. A convolutional neural network (CNN) model was then employed to classify motor faults based on these plots. Methodologically, the CNN was trained and validated across various GT configurations, including different image resolutions and color modes. Performance was measured using a 10-fold stratified cross-validation. The results demonstrated significant improvements, with the best GT configuration achieving an average accuracy of 98.43%, substantially higher than the traditional LightGBM method's 93.20% accuracy. The study underscores the efficacy of combining GT with CNN for enhanced fault detection, providing a robust alternative to existing machine learning and deep learning methodologies,

The increasing use of power electronics converters to integrate renewable energy sources has been a subject of concern due to the resonance oscillatory phenomena caused by their interaction with poorly damped AC networks. Early studies are focused on assessing the controller influence of a single converter connected to simple networks, and they are no longer representative for existing systems. Lately, studies of multi-infeed grid-connected converters are of particular interest, and their main aim is to apply traditional criteria and identify their difficulties in the stability assessment. An extension of traditional criteria is commonly proposed as a result of these analysis, but they can be burdensome for large and complex power systems. The present work addresses this issue by proposing a simple criterion to assess the stability of large power systems with high-penetration of power converters. The criterion has its origin in the mode analysis and positive-net damping stability criteria, and it addresses the stability in the frequency domain by studying the eigenvalues magnitude and real component of dynamic models in the admittance matrix form. Its effectiveness is tested in two case studies developed in Matlab/Simulink which compare it with traditional criteria, proving its simplicity.

In this paper, the steady-state behavior of the modular multilevel converter (MMC) is studied under balanced and unbalanced AC grid voltage conditions. The suggested mathematical model is derived combining the converter internal arm variables and both AC and DC grids variables. Moreover, the steady-state solution of the system can be achieved by setting the internal power balance within the converter. In order to verify the steady-state model, different types of AC fault conditions are imposed, and the output values are compared with the simulation results of an average model of the MMC circuit. The results show that the proposed analysis is in close agreement with the simulations for all conditions evaluated, validating the developed equations.