The dynamic features of microgrid operation, such as on-grid/off-grid operation mode, the intermittency of distributed generators, and its dynamic topology due to its ability to reconfigure itself, cause misfiring of conventional protection schemes. To solve this issue, adaptive protection schemes that use robust communication systems have been proposed for the protection of microgrids. However, the cost of this solution is significantly high. This paper presented an intelligent fault detection (FD) system for microgrids on the basis of local measurements and machine learning (ML) techniques. This proposed FD system provided a smart level to intelligent electronic devices (IED) installed on the microgrid through the integration of ML models. This allowed each IED to autonomously determine if a fault occurred on the microgrid, eliminating the requirement of robust communication infrastructure between IEDs for microgrid protection. Additionally, the proposed system presented a methodology composed of four stages, which allowed its implementation in any microgrid. In addition, each stage provided important recommendations for the proper use of ML techniques on the protection problem. The proposed FD system was validated on the modified IEEE 13-nodes test feeder. This took into consideration typical features of microgrids such as the load imbalance, reconfiguration, and off-grid/on-grid operation modes. The results demonstrated the flexibility and simplicity of the FD system in determining the best accuracy performance among several ML models. The ease of design’s implementation, formulation of parameters, and promising test results indicated the potential for real-life applications.

Modern fault location methods are robust; however, they depend strongly on the availability of the measurements given by Distributed Energy Resources (DER). If the communication or synchronism of this information is lost, the fault location is not possible. This paper proposes an adaptive impedance-based fault location algorithm for active distribution systems. The proposal combines information provided by Intelligent Electronic Devices (IEDs) located at the substation, the knowledge of the network topology and parameters, as well as the distributed power sources, to estimate the fault location. Its adaptive feature is given by the use of a Distributed Energy Resources (DER) electrical model. This model is used to estimate the DER current contribution to the fault, in case the information provided by a local IED is not available. The method takes two types of DER technologies into account: Inverter non-interfaced DER (INIDER) and Inverter-interfaced DER (IIDER). The proposed method is validated on a modified IEEE 34-node test feeder, which was simulated with ATP/EMTP. The results obtained using the IEDs information, presented a maximum error of 0.8%. When this information is not available, the method’s performance decreases slightly, obtaining a maximum error of 1.1%. The proposed method showed better performance when compared with two state of the art methods, indicating potential use for real-life applications.

This work analyzes hemodynamic phenomena within the aorta of two elderly patients and their impact on blood flow behavior, particularly affected by an endovascular prosthesis in one of them (Patient II). Computational Fluid Dynamics (CFD) was utilized for this study, involving measurements of velocity, pressure, and wall shear stress (WSS) at various time points during the third cardiac cycle, at specific positions within two cross sections of the thoracic aorta. The first cross-section (Cross-Section 1, CS1) is located before the initial fluid bifurcation, just before the right subclavian artery. The second cross-section (Cross-Section 2, CS2) is situated immediately after the left subclavian artery. The results reveal that, under regular aortic geometries, velocity and pressure magnitudes follow the principles of fluid dynamics, displaying variations. However, in Patient II, an endoprosthesis near the CS2 and the proximal border of the endoprosthesis significantly disrupts fluid behavior owing to the pulsatile flow. The cross-sectional areas of Patient I are smaller than those of Patient II, leading to higher flow magnitudes. Although in CS1 of Patient I, there is considerable variability in velocity magnitudes, they exhibit a more uniform and predictable transition. In contrast, CS2 of Patient II, where magnitude variation is also high, displays irregular fluid behavior due to the endoprosthesis presence. This cross-section coincides with the border of the fluid bifurcation. Additionally, the irregular geometry caused by endovascular aneurysm repair contributes to flow disruption as the endoprosthesis adjusts to the endothelium, reshaping itself to conform with the vessel wall. In this context, significant alterations in velocity values, pressure differentials fluctuating by up to 10%, and low wall shear stress indicate the pronounced influence of the endovascular prosthesis on blood flow behavior. These flow disturbances, when compounded by the heart rate, can potentially lead to changes in vascular anatomy and displacement, resulting in a disruption of the prosthesis-endothelium continuity and thereby causing clinical complications in the patient.

Este trabajo propone un sistema de detección basado en radar para calcular las orillas de un rio usando procesamiento de imágenes. El sistema estima orillas sin pérdida apreciable en resolución evitando costos asociados con batimetrías y/o imágenes satelitales. El sistema está compuesto por un radar comercial, un GPS, y un sensor de rumbo que se comunican a un nodo central que georrefencia las mediciones del radar y ejecuta los algoritmos de procesamiento de imágenes. El hardware se basa en un FPGA para interactuar con la señalización interna del radar y extraer la información requerida. El nodo central implementa un novedoso modelo para georreferenciar las mediciones en coordenadas WSG-84. Posteriormente, un algoritmo de tipo “seam-carving” se aplica sobre las coordenadas estimadas para crear las orillas basándose en la probabilidad de la medición. El sistema se valida usando información oficial, y los resultados muestran un ECP de hasta 6 m.