Numerous applications use power conversion by applying DC/DC converters with wide transformation ranges and capacities to provide high current or voltage levels. Low voltage applications have been standardized to 48/24/12/5 V for various Electric Vehicle applications such as lighting, GPS, audio and air conditioning. However, for fast charging of electric vehicles, high voltage levels standardized to 380 V are required. Some requirements to be satisfied by DC/DC converters are high transformation ratios, high power density, high efficiency and low current and voltage ripple at the output. This paper presents the design and modeling of a buck-boost converter based on the concept of reduced redundant power processing with high voltage transformation ratio. The operating states of the converter during the switching process of the switches are presented. The average and linear models are presented, as well as some comparative aspects with some topologies reported in the literature.

Detecting disturbances using digital signal processing methods and techniques that allow the correct extraction of their distinctive characteristics to make the classification more effective is necessary for Power Quality monitoring. But developing an automatic detection system to be applied in smart measurement devices is not a trivial task, especially in obtaining a low computational cost method that can be integrated into hardware, due to the need to coordinate the functions of data acquisition, preprocessing, detection, and data exchange in real-time. It has been demonstrated that FPGA is a sufficiently fast hardware platform that allows the detection of disturbances of transient nature. In this work, a methodology for detection and extraction of the distinctive features of seven simple power quality disturbances based on Discrete Wavelet Transform and methods of energy and RMS values extraction, implemented in real-time using the Xilinx Artix-7 FPGA from Xilinx, is proposed. From implementing the proposed methodology on the hardware platform, the result obtained is an algorithm that allows extracting the distinctive features of the analyzed disturbances, making optimal use of memory and processing resources, which makes this procedure efficient for its implementation in real time.

Power electronics converters that conditions electric power in its DC form, and that are capable of reversing power flow are becoming increasingly essential in a variety of applications such as energy storage systems, electric vehicles, and renewable energy systems. Among them, non-isolated DC-DC bidirectional converters may be simpler and have higher power densities than isolated converters. This is because they do not require a transformer and can consist of a single power stage, making them suitable for applications that do not require a high level of safety or galvanic isolation. The fundamental topology of a non-isolated DC-DC bidirectional converter is derived by replacing the unidirectional switches of a basic converter with bidirectional switches. However, modifications, improvements, and new topologies have emerged aimed at reducing weight, volume, losses, and cost, while increasing reliability and power density. This work reviews and compares some reported bidirectional non-isolated power converter topologies and their applications.

Power-voltage characteristic curves are a widely used tool for determining voltage stability margins in a power system. They are typically used for steady state and offline studies, for system expansion objectives and voltage collapse evaluation; however, the higher growth in load demand and insufficient infrastructure to transmit energy, has caused the voltage collapse phenomenon to be common in power grids. In this regard, this article presents the performance of the pseudo arc length continuation power flow method, for the tracing of PV curves in an industrial controller in real time. The objective is to evaluate whether current industrial devices have the ability to perform the tracing of PV curves and, consequently, use this power system analysis tool in real time. The performance evaluation of the continuation method is performed on a SEL- 3555, for the 39 buses benchmark of New England. In addition, to prove the reliability of the results, a comparison is made with professional simulation software such as PSS/E.

The tequila and agave culture are important symbols of the national identity in Mexico. In the last decade, the production of tequila has increased and improved the care of Tequilana Webber Blue Agave. In this paper, we introduce a methodology based on the use of Unmanned Aerial Vehicles (UAV) as drones, image processing techniques and machine learning approaches for the detection of agave plants. Several landscape images of agave crops are collected from the Romita region in Guanajuato state. These images are processed to obtain orthomosaics which allow us to study all the target region of the agave crops. The orthomosaics are segmented using superpixels techniques in order to define features which allow us to recognise the agave plants. For experimental purposes we compare five segmentation methods in order to evaluate the performance for the detection of agave plants. Random Forest Algorithm is implemented for classification purposes. Our proposal achieves 97% of accuracy to detect the agave plants.

The objective of this paper is two-fold: (i) to introduce a full formulation for the calculation of three-phase impedance matrices of underground cables with the explicit representation of the self and mutual impedances that exist between the metallic components of the cable, including bonding and non-bonding cables; (ii) to carry out a power loos analysis in three-phase power systems of underground cables with shunt reactive power compensation, using a full three-phase formulation.

In this paper, we consider the problem of voltage regulation of a proton-exchange membrane fuel cell (PEMFC) connected to an uncertain load through a boost converter. We show that, in spite of the inherent nonlinearities in the current-voltage behavior of the fuel cell (FC), the voltage of an FC/boost converter system can be regulated with a simple proportional-integral (PI) action designed following the passivity-based control (PBC) approach. We show that for all positive values of the controller gains, the voltage converges to its setpoint. An Immerse & Invariance parameter estimator is afterward proposed which enables the voltage regulation of the PI passivity-based control when the load varies.

This document presents the simulation results of a novel inverse kinematic-based controller which is time-invariant. The proposed controller addresses the person-following robot problem, in order to achieve a social robot motion a modulation of controller gains is proposed, to modify the velocity response of a differential drive mobile. According to the results, our proposal has good performance.

Speech recognition is a common task in various everyday user systems; however, its effectiveness is limited in noisy environments such as moving vehicles, homes with ambient noise, mobile phones, among others. This work proposes to combine deep learning techniques with domain adaptation and filtering based on Wavelet Transform to eliminate both stationary and non-stationary noise in speech signals in automatic speech recognition (ASR) and speaker identification tasks. It demonstrates how a deep neural network model with domain adaptation, using Optimal Transport, can be trained to mitigate different types of noise. Evaluations were conducted based on Short-Term Objective Intelligibility (STOI) and Perceptual Evaluation of Speech Quality (PESQ). The Wavelet Transform (WT) was applied as a filtering technique to perform a second processing on the speech signal enhanced by the deep neural network, resulting in an average improvement of 20% in STOI and 9% in PESQ compared to the noisy signal. The process was evaluated on a pre-trained ASR system, achieving a general decrease in WER of 14.24%, while an average 99% accuracy in speaker identification. Thus, the proposed approach provides a significant improvement in speech recognition performance by addressing the problem of noisy speech.

In this work, a comparative evaluation of embedded devices for Natural Language Processing (NLP) in biomedical applications in Spanish is carried out, specifically in text-to-speech and speech-to-text algorithms. Several embedded devices have been selected to achieve this, including Jetson Nano, Raspberry Pi (4B, 400, and 3B+), and Latte Panda. This analysis focuses on key aspects such as execution time, CPU usage percentage, and RAM usage percentage. These criteria will allow us to compare the evaluated devices' performance and determine the most suitable NLP in biomedical applications in Spanish. This benchmarking is expected to provide valuable information to facilitate the selection of devices in future projects and applications within the NLP. This work will allow professionals and developers to make informed decisions and optimize their resources when implementing PLN solutions in the biomedical field in Spanish.

Fault detection and condition monitoring of induction motors (IMs) are of paramount importance for the industry. In this regard, researchers around the world have developed different methods to monitor and detect various types of damages in IMs, such as damaged bearings, misalignment, and broken rotor bars (BRBs), among others. In particular, the detection of a BRB has received special attention as it is difficult to detect at an early stage and can quickly evolve into catastrophic damages if not detected in a timely manner. As a contribution to this issue, this work explores the potential of the Hjorth parameters as indicators of BRBs using the current signals of an IM under different operating conditions, i.e., healthy (HLT), half BRB, one BRB, and two BRBs. Obtained results show that the Hjorth parameters are sensitive to the previously mentioned conditions, allowing the proposal of pattern recognition schemes for automatic classification. For this task, the k-means clustering method is proposed in this work because of its easy implementation. The obtained results demonstrate that the proposed method is reliable to monitor the IMs condition, reaching an accuracy of 98.75%

Power Hardware in the Loop (P-HIL) systems and Battery Energy Storage Systems (BESS) are essential tools in the transition to a more sustainable and efficient energy matrix. These systems work together to analyze the integration of intermittent renewable energy sources to improve the stability and reliability of electrical grids. This, in turn, contributes to the reduction of greenhouse gas emissions and the development of a low-carbon economy. This paper presents an experimental working platform and real-time simulation based on P-HIL technology and scaled power electronics prototypes. The platform allows the analysis of the interaction of BESS devices in electrical distribution grids. A case study is presented to demonstrate the combination of two main qualities of a BESS: improving voltage stability and reducing peak demand. To this end, an experimental 1 kW BESS consisting of a Dual Active Bridge (DAB) and a Voltage Source Converter (VSC) is connected to a 13-bus IEEE distribution network. The attained results demonstrate the ability of the platform to bridge two areas of electrical engineering and highlight its significant advantages.

The transmission grid transports the electricity that is traded between different suppliers and demanders in a market environment. Currently, these electricity suppliers have increased their generation investments in renewable technologies such as wind and photovoltaic, whose ideal locations are in places far from the major centres of demand, in this scenario is that it must have adequate transmission capacity, Otherwise, there will be an increase in the cost of electricity, or the collapse of the system, which is why it is necessary to have a plan for the expansion of the transmission network capable of finding the best configuration of the electricity network with the lowest investment cost to meet the load forecast and take advantage of the development of new technologies. In this paper, a model is proposed to evaluate the integration of battery-based storage systems in transmission grid expansion planning. The model is validated on the six-node Garver system and tested on the 24-node IEEE-RTS system.

This paper shows the Takagi-Sugeno convex compensator design for a class of nonlinear system, that consist of liquid level system of a two-tanks interconnected for which the levels are controlled through two electro-valves, by means of the convex controller; for the purpose of further work to perform fault diagnosis, the design of a Takagi-Sugeno convex observer is tested. The gain scheduling functions used are Gaussian Functions. For the stability analysis of the linear subsystems and the computation of the compensator gains, the interior point method is used in the solution of Linear Matrix Inequalities (LMI's), including a proposal to bound the gains. The contribution of the present work is that the design of the compensator was successfully tested in real-time experimentation using Arduino-Matlab and in simulation in Matlab-Simulink.

This paper presents a comparative analysis of transformerless power converters based on the conventional fullbridge converter. The comparison is aimed at Transformerless converters to mitigate leakage ground currents in PV systems. The converters compared in this paper are the H5, HERIC, and DD-Buck converters, which are controlled under three different modulation schemes to evaluate their performance indexes. The main performance indices analyzed in this work are efficiency, Total Harmonic Distortion in current and voltage, and Leakage Ground Currents. Finally, experimental and numerical results are performed to assess the proposed study.

This paper presents the most important characteristics and dimensional criteria when specifying a Battery Energy Storage System (BESS). Rated energy and power capacity values and their meaning in different measurement points are discussed. Both system and individual subsystem efficiency in different operation points is considered. Battery lifetime definitions are presented and their relationship to the above characteristics discussed. Finally, an example design process with a specification is presented.

The integration of electric induction machines into digital platforms has gained significant relevance in recent years. Field-Programmable Gate Arrays (FPGAs) provide a flexible and reconfigurable hardware platform for implementing digital control systems and digital system emulators. This work presents an implementation of an electric induction machine in a digital description and investigates the effects of injecting faults into the system. The study aims to demonstrate the importance of digital implementations and fault injection techniques in enhancing the performance, reliability, and fault tolerance of electric induction machines.

This paper presents a comparative study of five cutting-edge iterative methods - Particle Swarm Optimization, Quantum Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Damped Least Squares (DLS), and Forward and Backward Reaching Inverse Kinematics (FABRIK) - used to solve the challenging inverse kinematics problem in robots with increasing degrees of freedom in their kinematic chains. The analysis includes 7, 9, 11, 13, and 15 degrees of freedom redundant serial robots with hinge and pivot joints. Performance indicators, such as execution time, iteration count, and final error, were evaluated for each method across 500 randomly generated target poses for the five robotic chains, providing valuable insights into the algorithms' behavior as the degrees of freedom in the kinematic chains increase.

The evolution of modern electrical networks, with bidirectional power flow and interconnected renewable energy sources, presents challenges for traditional automatic capacitor banks in effectively correcting the power factor. In some scenarios, industrial installations may experience nearly zero power consumption periods due to high power generation levels. As a result, the steps in automatic banks can become disproportionately large, making it difficult to achieve proper compensation. To address this issue, a proposed 63-level asymmetric capacitor bank offers a high-resolution solution without discharge resistors and reduced transients. By significantly increasing the number of steps, the bank provides greater resolution in power factor correction, even during periods of near-zero power consumption. Its intelligent startup systems enable controlled connection and disconnection, eliminating the need for discharge resistors and minimizing voltage and current transients. This adaptive solution ensures improved power factor correction and network stability in industrial settings with fluctuating power generation and consumption. Moreover, the bank's ability to accommodate bidirectional power flow and interconnection of renewable energy sources, such as photovoltaic panels, further enhances its suitability for modern electrical grids.

Fossil fuels are widely used for power generation, but they are increasingly scarce, polluting, and expensive. For this reason, the incorporation of energy generation from renewable sources is increasing at an accelerated rate in recent years in the electricity sector. The injection of large amounts of wind and photovoltaic energy into the electrical system must consider the changes that the stability of the system may undergo, being the frequency stability directly affected by the conservation caused by renewable generation and therefore strategies must be implemented to guarantee the safety and reliability in the operation of the electrical system. The main contributions of this paper are: 1) the implementation of an observer-based state feedback control, to achieve a convenient relocation of the power system poles and improve the frequency behavior of the system, so that it reaches the steady state without the presence of oscillations and in a shorter period of time; 2) the implementation of the super twisting controller to compensate for the intermittency of renewable generation in the power system and whose action contributes significantly to the system frequency stability, achieving rapid convergence in finite time and without steady state error. For the case studies, the results were satisfactory, demonstrating that both control techniques are highly efficient.

The article proposes an adaptive control law to compensate input faults in an electromechanical actuator aiming to restore the output to a desired trajectory. The adaptive control approach explicitly considers in the design both Coulomb and viscous friction, using a smooth model for the static friction term. The proposed adaptive algorithm proves to be useful even in the case of parametric uncertainty, rendering the system globally asymptotically stable. Through simulations, the authors demonstrate the efficacy of the proposed scheme, showcasing the performance under parametric uncertainty and input fault.

The characterization of conductive and insulator materials plays an essential role in biomedical engineering. A technique is to measure the difference in conductivity of the domain and compare it with a reference material. The present work analyzes the resistivity difference between a copper and PVC tube. The data acquisition was obtained by the LatePanda device integrated into an Arduino board coupled to a phantom of 16 electrodes located on the periphery. Electrical Impedance Tomography (EIT) was used to measure the resistivity changes. The images showed a significant difference between conductive and insulating material. The obtained information from the test will be used in future works to determine the varying of resistivity amongst a healthy and one cancer tissue

Abstract—According to the World Health Organization, breast cancer is defined as the abnormal and disorganized growth of cells in breast tissue. It is currently one of the biggest challenges facing health systems worldwide. Making a precise diagnosis is essential to offer the most appropriate treatment adapted to each patient condition. Computer vision-based tools could help the specialist in the diagnosis task. One major challenge in digital histopathology is the wide variation in staining tissue. This work assesses the performance of color normalization under different approaches, measuring its impact on the structures of interest related to the graduation of tissue samples. It is also analyzed how color normalization could improve relevant structures segmentation, particularly nuclei, an essential element in the Nottingham Graduation System followed by pathologists. Four methods of color normalization were implemented, and their respective structural and color-associated metrics (SSIM, PSNR, and colorfulness) were calculated. After color normalization, a segmentation process was performed using the STARDIST tool. In general, an improved segmentation was observed for the Reinhard and Stain-Net methods of color normalization.

The deep of hypnosis regulation has been studied for several years to help and assist the anesthesiologist in various general surgery processes. The present contribution presents a scheme for automating this process considering the infusion of two drugs, remifentanil, and propofol, as control variables and the bispectral index as the output variable. A description of virtual compartment-based dynamical modeling for both drugs is presented. Using this model, a proposal for closed-loop regulation based on a discrete model predictive control is presented. The proposed algorithm is tested in twelve virtual male patients, where the controller simulates and rejects two types of disturbances.

La corriente de Inrush es un fenómeno transitorio de baja frecuencia que se presenta por las conmutaciones del transformador, ya sean monofásico o trifásico, así como en transformadores de instrumentación, como los Transformadores de potencial (TP´s). La importancia en el estudio de este fenómeno radica en las grandes magnitudes de corriente que se pueden presentar, ocasionando un mal funcionamiento de la protección diferencial, porque la corriente Inrush puede ser mayor a la corriente nominal hasta diez veces dependiendo del ángulo de incepción, siendo similar a la magnitud de una corriente de falla. En este sentido, la corriente de Inrush es un fenómeno que se presenta en cualquier transformador lo que hace importante su análisis y mitigación y así evitar daños importantes a equipos dentro y en el sistema eléctrico. En este trabajo se lleva a cabo un análisis de la corriente de Inrush en un banco de transformadores trifásico de 9 kVA utilizando la técnica Taylor-Fourier para el análisis del contenido armónico de las señales de corriente.

In order to facilitate the design of classical controllers, modeling functions for unknown control systems is an important goal in control engineering. Therefore, the Recursive Least Squares (RLS) technique is the most recognized method for system identification. In the presented work, the digital architecture of the reprogrammable RLS algorithm for the identification of control systems is proposed, reducing its components by developing the relevant equations for the calculation of the coefficients and minimizing the consumption of resources within a reprogrammable FPGA EP2C20F484C7N board. The proposed embedded system consumes 19% of the logic units and 29% of the multipliers of this device and can also work at a maximum operating frequency of 36.69 MHz.

Phasor measurement units are widely used for monitoring purposes in power grids. Over the years, different phasor estimation algorithms have been studied to obtain more accurate estimations. Following this path, this paper specializes in developing a real-time phasor estimation via the discrete time Taylor-Fourier transform (DTTFT) implemented through their O-splines on a field-programmable gate array (FPGA) board, based on the finite impulse response structure. A digital Taylor-Fourier filter was designed using the O-splines of the DTTFT to deal with the extraction of the dynamic phasor estimates such as amplitude and phase. The description structure was developed using a multiplier accumulator (MAC) structure, which only uses four embedded 9-bit multiplier elements for an 18-bit input-output resolution. To assess the performance of the system, steady-state and real-event scenarios are analyzed by an FPGA-in-the-loop simulation using the Matlab/Simulink software. The results show that the DTTFT-powered phasor estimator could be successfully described using VHSIC hardware description language (VHDL) code and implemented in a D2-115 board by Intel.

This paper presents a 1.5-kW active power rectifier that is intended to be the power topology used in a Battery Charger for a 5.6-kW battery pack that is used to propel an 83 kWh Sport Motorcycle. Experimental results demonstrate that the converter topology and the control strategy can keep a 0.99 Power Factor value, reducing the THD in each phase of the AC power supply. The controller is evaluated applying current reference steps and load changes at 1.5 kW power rate operation.

This paper presents the application of a Super Twisting Controller (STC) based on state and disturbance observers that allow the efficient operation of the boost converter under load variations. The state estimation allows the elimination of sensors in the system which reduces its cost, while the perturbation estimation allows through a rearrangement of the system to adjust to changes in the converter load. A STC is proposed to reduce the chattering in the control signal since a high value, outside permissible limits, leads to deterioration of the actuators. A Lyapunov stability analysis of the proposed observer-based controller is performed. Finally, to verify the proper operation of the proposed scheme, simulation tests are presented and the results are compared with a conventional technique based on PI-controllers.

This paper shows the settings and evaluation of four protection schemes with four different shunt capacitor bank configurations, also units with external fuses and fuseless. Internal and external faults on shunt capacitor banks (SCB) was taken to evaluate and software PSCAD/EMTDC was used to simulate this. The test systems on this paper are two substations on Mexico electrical system on 23 kV with presence of shunt capacitor banks, called SE Moctezuma and SE Tuzania. The results show critical conditions where protection schemes operate properly. Nevertheless, some scenarios evidence a null operation of protection schemes even with several units or elements faulted. Finally, the evaluation on these schemes demonstrates that methods with voltage operating principle require a less sensitivity than schemes witch current operating principle

Both the transition from grid-tied mode (GTM) to islanded mode (ISM), denoted as GTM-ISM transition, and the transition from ISM to GTM, denoted as ISM-GTM transition, are one of the main topics in AC electrical microgrid (MG) research. Within the MG, the grid-forming power converter (GFRC) is responsible to carry out both the GTM-ISM and ISM-GTM transitions. Usually, the GFRC includes a power supply source at the DC bus; in most cases, this DC source is a battery pack. In abnormal mains conditions, the MG must operate in ISM, then, the GFRC uses the battery power to generate the voltage at the point of common coupling (PCC) during the GTM-ISM transition; in the same way, the GFRC uses the power from the battery pack to sustain the PCC voltage during ISM and for synchronizing it when the nominal conditions of the mains are recovered. The transition of MG operating modes with a battery-less GFRC in the literature has not been reported. Thus, this paper presents a control scheme proposal to perform both GTM-ISM and ISM-GTM transitions with a battery-less GFRC. The results showed the capacity of the battery-less GFRC to operate as smart load during both GTM-ISM and ISM-GTM transitions. This battery-less GFRC proposal highlights a contribution within the research of GFRC units, since the GFRC operation has always been reported using a power supply source at the DC bus, mainly battery packs, but not using a battery-less GFRC working as a smart load for the generation of the PCC voltage.

A power plant interconnected to the power system must provide reactive power under disturbances or as required by the system operator. Wind parks based on doubly fed induction generators technology can compensate reactive power by voltage control at the wind park terminals from three control modes. This technique is proposed in this paper, in order to the wind park can provide support the voltage of the electrical power system and achieve the reactive power requirements of the Mexican grid code. To this end, different simulations of a 75 MW wind park interconnected to an equivalent power system are carried out, in which different scenarios are used with the three control modes. The results of the simulations are evaluated to verify compliance with the minimum reactive power requirements in agreement with the established of the grid code.

An alternative to estimate the actual resonant frequency in induction heating systems is presented. The output current is measured and processed by a digital device in order to obtain the amplitude of the first and third harmonic components. The proposed analysis and solving equation rely on the equivalent impedance relationship at two different frequencies. Sampling rates in the signal processing are discussed while simulation results show the feasibility of the proposal.

This work presents the development of a small signal model for buck-boost four port DC-DC converter (BB-FPC), which can be used to integrate distributed energy sources (DES) and energy storage systems (ESS). This converter integrates four ports, three for sources and one for an isolated load. Power flow between the sources is bidirectional allowing them to work as sources or sinks to integrate storage systems, which are controlled by two duty cycles and a phase angle. The manuscript presents a small-signal model that describes the interaction between the three controllable inputs and the voltage and currents in each port.

Due to the increasing demands for efficient and flexible manufacturing processes in the era of Industry 4.0, the integration of augmented reality (AR) technology has emerged as a promising solution to enhance the training of assembly line operations. In this article, we present the design and implementation of an AR-based assembly line for Industry 4.0 applications. By utilizing Unity as the development tool, we created an AR application for the training of manufacturing engineering students and workers that enables them to visualize and interact with virtual components overlaid in the real-world environment by using their mobile phones or tablets. Through a user-centered design approach, we optimized the user interface and interaction design to ensure an intuitive and user-friendly experience for workers and students. Furthermore, we discuss the implications that our findings have in the context of Industry 4.0 applications, highlighting the potential of AR technology in revolutionizing assembly line operations, thus contributing to the advancement of Industry 4.0 applications by harnessing the capabilities of AR technology to enhance assembly line operations, improving worker’s productivity, students’ training, and drive innovation in the manufacturing industry. The developed AR assembly line might offer a range of benefits, including improved efficiency, reduced errors, and enhanced training capabilities. The application has a strong potential to be used to provide real-time.

This document presents the simulation results of a new inverse kinematics-based controller for path tracking applied to a mobile robot, specifically a car-like robot. The control law design addresses the limitations of steering rotation and the limited speed of the robot. It also takes into account that the reference point is located at an arbitrary position inside or outside the robot’s body. The trajectory is obtained through a planning process that takes into consideration the limitation in steering rotation. In this context, the simulations demonstrate excellent performance of the controller both in steering control and robot velocity.

Originally, Lyapunov’s Theory was specifically conceived to analyze closed-loop systems with no control inputs. If systems with control inputs are considered, the approach based on Control Lyapunov Functions (CLF) can provide analysis facilities. A CLF is a candidate to be a Lyapunov Function (LF), thanks to the fact that its derivative can become negative through the appropriate choice of control input. The existence of a CLF is sufficient (and necessary) to asymptotically stabilize a nominal system. This paper presents a procedure through which a CLF can be obtained that allows proposing control schemes for voltage regulation in DC-DC Boost Converter modeled by Hamilton’s equations as a case study. Obtaining this type of regulators makes it possible to ensure the asymptotic stability of the system.

This paper adopts a methodology to assess and rank the severity of the most frequent disturbances, 3 types in particular, that occur in the Mexican Interconnected System (MIS) when wind power plants (WPPs) are integrated. This is done by combining three stability indices from which an overall performance measure can be derived. In this way, it is possible to assess the severity of the disturbances such as generation tripping, line tripping and disconnection of loads. The indices under consideration include measures such as maximum amplitude, speed variation, and the Lyapunov exponent. For ranking the severity, a general index value is used. To confirm the effectiveness and performance of the proposed methodology, this paper investigates the 190-bus and 46-generator MIS equipped with an 8 % of wind generation to quantitatively evaluate the severity of disturbances, resulting in 66 % of unstable disturbances which are associated with the disconnection of loads.

The detection of states in the driver is important for the prevention of traffic accidents, because, unfortunately, the accidents caused by undesirable state in drivers happen around the world and these must be dealt immediately. Drowsiness in drivers is one of the most important factors causing traffic accidents, especially on highways. In this paper, we propose models based on machine learning techniques to predict driver states by extracting ten facial features. We use the MediaPipe Face Mesh for the detection of relevant points in the face, and then extract ten facial features related to the driver’s state. We apply the machine learning techniques to effectively predict the four driver’s states, which are normal, talking, yawning, and sleeping. The machine learning techniques used are K Nearest Neighbours (KNN), Support Vector Machine (SVM) and Long-Short Term Memory (LSTM). The effectiveness of each technique is compared using two public databases.

This work presents the design and implementation of a low-cost current source based on power electronics for testing a power current relay. The topology for the system considers a three-phase two-level inverter working as three individual half- bridge single-phase converters. Additionally, a passive filter based on the LCL topology mitigates the high-frequency components of the current signals. The inverter uses the wide bandgap transistors GaN (Gallium Nitrite) type, which allows working with high efficiencies and switching frequencies to achieve low values passive components. The validation for the proposal considers experimental testing, taking quality measurements of the current signals, and testing the power relay SEL-451, by setting the protections 50 and 51.

Ladybird beetles, also known as ladybugs, are a diverse family of small, brightly colored beetles with thousands of species around the world. Their geographic distribution and their impacts as invasive species on other endemic populations are still not well understood. The ability to accurately identify and study ladybird beetles is essential for effective management and conservation efforts. In this paper, we propose a novel method for the detection and segmentation of ladybird beetles in natural environments. Our approach combines the YOLOv8 object detection model and the "Snakes" active contour model to achieve precise detection and segmentation of ladybird beetles. We evaluated our method on a dataset of 2300 ladybird beetle images from the iNaturalist project, obtaining a DICE score of 84.82% and an IoU score of 73.73%. These results demonstrate the effectiveness of our approach in accurately identifying and segmenting ladybird beetles. Our method has potential applications in biodiversity research, invasive species management, and ecological monitoring.

In this paper the simulation of a mechanical coupled of two permanent magnet machines is realized, one of them acts as a traction machine in a traction system, the other one generates the opposite torque according to the vehicle electric dynamic equation. Although the regenerative braking system is a trend in electric traction systems, a dynamic braking system is simulated because this paper does not contemplate the bidirectional DC/DC converter for regenerative braking. Additionally, the mathematical model of the permanent magnet machine is obtained, allowing to apply the field-oriented control, moreover the space vector modulation pulse width technique is applied on the inverters used. Finally, the simulation is done with the SAEJ227 driving cycle that contains a constant acceleration period, a constant speed period and a braking period allowing obtain the vehicle behavior on these stages, showing the results as the speed response, the opposite torque developed by the electric vehicle emulator and the action of the dynamic brake.

This paper is devoted to study a new statistical description based on the polynomial distribution function for the reliability of electric power systems. An electrical system can be represented as a structure composed of many components, parts and blocks, each of which ages and tends to fail. That is why preventing failures is especially important in electric power systems, where both industry and the population depend on the generation and constant delivery of energy. Any failure in the system affects a large number of consumers. Each system component is characterized by its time-dependent hazard function (or failure rate). Some of its statistical properties are discussed, method of moments and Weibull distribution are used for estimating the parameters. A simulation study is performed to compare the performance of each method of estimation. Finally, for the description of the reliability of electrical systems, it was found that the representation of failure times by a distribution function with polynomial failure rate is more suitable according to the Kolmogorov-Smirnov (K-S) criterion.

Social Human-Robot Interaction is a research field focused on studying how should be the behavior of a robot when sharing the environment with human users. This way, the socially appropriate motion in mobile robots is an important objective to achieve in control tasks such as autonomous navigation, simultaneous localization and mapping, person-following robot, and so on. Thus, this paper presents some simulation results of our strategies to achieve the socially acceptable robot motion under the task of the person-following robot from the control theory point of view. A differential drive mobile robot with an onboard camera is used to model the task. The simulation results show a good performance when the proposed task is achieved.

The integration of variable renewable energy sources (VRES) into power systems has brought about changes in the operational control requirements of electric power grid. Voltage regulation and management of reactive power resources are crucial to maintain power system efficiency, quality, reliability, continuity, safety, and sustainability. In this context, the power system operator is responsible for defining and coordinating the necessary actions to maintain both the voltage within the established ranges and an adequate reserve of reactive power to support contingencies and/or failures in the network. This paper evaluates the impact of defining the prioritized reactive power control mode for reliability on VRES. Two case studies are presented to compare the performance of voltage regulation with the three possible modes that are reactive power setpoint, voltage control, and power factor setpoint. The results show that prioritizing the reactive power control mode can significantly enhance the performance and reliability of power systems with VRES integration, particularly during dynamic and fault conditions. The findings of this study can provide useful recommendations and operational strategies for power system operators to improve the reliability and safety of power systems with VRES integration.

In this article, the commissioning of an Intellirupter by S&C Electric Company in the medium voltage network of a primary distribution feeder belonging to the Electric Company Azogues, is explained. The feeder 124, operating at 22kV with a maximum demand of 2.04MVA, has been taken as a case study. The commissioning of the equipment was carried out after coor- dinating protections based on fault parameters, parameterizing the different analog and digital signals to be controlled, and integrating it into the SCADA system of the Electric Company. By implementing this equipment, it is expected to reduce power supply interruption times and improve reliability indices. These results will be reflected in the long term through a comparative analysis with historical FMIK and TTIK indices.

In this paper, a basic boost converter is analyzed and designed as a characterization system for photovoltaic modules, where the energy generated in the characterization process is recovered in a battery. Under the scenario of photovoltaic application and storage, the steady-state operating condition, voltage conversion ratio, design expressions of passive elements and the converter operation mode are derived. The operation of the converter as a characterization device with energy storage is verified through computational simulations.

Melanoma is an aggressive skin cancer that can rapidly spread to other parts of the body if not diagnosed and treated promptly. Current diagnostic methods include visual evaluation, biopsy, and histopathological analysis, but can be subjective and require significant time and resources. This work proposes the development of a melanoma classification protocol based on small and large MobileNetV3 architectures combined with two fine-tunning schemes. MobileNetV3-based models were used with transfer learning, and fine-tuning schemes were incorporated into the base model for melanoma classification. The best performance was achieved by the large MobileNetv3 architecture with the fine-tuning 2 schema. Training evaluation on 2003 images reported a successful mean of the area under the receiver characteristic operating curve score of 0.906. Additionally, the test on 223 images provided a reasonable score of 0.917. Both results were obtained using a stratified ten-fold cross-validation mechanism. The best model was implemented on two mobile emulators to analyze its feasibility according to power consumption, and the obtained mean of 0.45 mAh per image constituted a high-quality performance. Furthermore, it was implemented in a web app, and the average response time of 115.44 ms with an average of 15kb transferred over the network per image demonstrated efficient utilization of computational resources. Therefore, developing and deploying successful deep CNN models with transfer learning into limited-resource devices is possible as a helpful second opinion tool for early patient self-diagnosis of melanoma.

Data-driven, reduced-order models of power system frequency control are becoming important analytics tools to obtain fast assessments of dynamic security in power systems. However, these models are commonly associated with low precision in terms of representing more complex phenomena, such as intentional islanding or load and generation shedding schemes. In this way, its applicability to predict or study extreme frequency events has been questioned. This work presents a data-driven algorithm to represent load/generation shedding events using a reduced-order model of frequency dynamics. The applicability of the model is examined by considering an actual case of a cascade load/generation shedding event in the Chilean power system.

sEMG (surface electromyography) is the de-facto biopotential medical instrumentation solution for non-invasive detection of limb motion EMG activation signals, which in turn provides important information regarding intentional and reactive muscle activity. Thus, sEMG data analysis plays an essential role in rehabilitation. However, sEMG delivers information of electrical activity mainly due to superficial muscles over a wide sensing field. Additional physiological processes may be elucidated by combining sEMG data with other measurements, such as Electrical Impedance Tomography (EIT) imaging, to assess muscular activity due to increased vascularization of the muscle. In particular for multimodal measurement systems and, as with any biomedical instrumentation system, the sensing electrode array influences the quality of data measured and requires careful examination of the signal properties to be studied in order to select the appropriate type of electrode set. It is common to find that “wet”, AG/AgCl electrodes are used for sEMG measurements, whereas “dry”, (i. e. stainless steele, copper-nickel) electrodes are preferred for electrical impedance measurements since the conducting layer of wet sensors introduces a low-pass filter effect in the β-dispersion frequency range. Here, the authors examine sEMG data obtained for electrodes of different materials in comparison with the preferred Ag/AgCl sEMG electrodes, towards selecting a multimodal wearable measurement setup for assessment of upper limb motion. sEMG data was obtained for 10 healthy volunteers. The datasets were analyzed using STFT (Short Time Fourier Transform), PCSA (Principal Component Spectral Analysis) for PCR (Principal Component Regression and PLSR (Partial Least Squares Regression). The results indicate that Steele Electrodes perform closer to the preferred Ag/AgCl electrodes.

This paper presents the application of a time-domain (TD) method for the solution of equations of discrete equivalent models. This approach consists in the substitution of a discrete equivalent Norton model (DNEM) for electric components, which allows obtaining a reduction through companion-circuit branches. The companion-circuit branch is obtained from numerical integration rules, e.g. backward Euler (BE) and trapezoidal rule (TR). This formulation is based on companion-circuit analysis (CCA). The CCA is of general application regardless of the size of the system and consists of algebraic equations, whose solution can be obtained by sparse matrix factorization using a LU decomposition process. The efficiency of the sparse CCA-BE-LU and CCA-TR-LU methods is demonstrated through the determination of the periodic steady-state solution of small, medium and large-scale power systems under different states of operation, such as harmonic distortion and the presence of faults. These methods allow fast and accurate, computational solutions, whose performance is compared against the obtained results with the PSCAD/EMTDC® simulator.

Generally speaking, design methodologies are system oriented. To the best of our knowledge theres is not a design methodology for IoT systems in the application domain of agriculture. The closest methodologies were conceptualized for broader areas such as software engineering, network design, cyber-physical systems, and IoT from a cross and indepentent domain perspective. Initial steps have been done to provide specific design methodologies for other domains including Industry 4.0, health and intelligent transportation systems. However, as expected they only capture and address the specifics of their domain. We believe that the same engineering design approach should be followed for the other IoT domains. In this paper we propose a design methodology for IoT Systems in the application domain of agriculture.

In this paper, a real time simulation based on software in the loop (SIL) of a battery energy storage system (BESS) integrated into the distribution electrical networks is presented. The BESS is used for the active and reactive power control, and as an interface between the battery bank, DC-link and voltage source converter (VSC), generating an independent control of the DC voltage and reactive power in the electrical networks. This system allows a bidirectional power flow, so that it can store energy when the generation exceeds the demand, but if the opposite occurs the energy is supplied. The effectiveness is assessed by complete mathematical model, the simulation results are evaluated using MATLAB-Simulink® (Matlab r2018, Mathworks, Natick, MA, USA) and are validated with the real-time simulator Opal-RT Technologies® (Montreal, QC, Canada).

Finding solutions to NP-Complete problems is colloquially related to finding a needle in a haystack because of their complexity, which, in consequence, yields exponential time algorithms. In particular, one strategy to find ``good solutions" to these problems is to evaluate potential solutions generated at random and measure the quality of each in every attempt. However, true randomness cannot be implemented on classical computers. Hence it is emulated through algorithms that create pseudo-random numbers. This paper analyzes two NP-complete problems in graphs: the Traveling Salesman and the Hamiltonian Path Problems. The Ant Colony Optimization algorithm was used to find solutions to these with different random number generators: pseudo-random and quantum-random. The convergence time and overall cost of varying graph setups (different complexity levels, i.e. 50, 100, 150, and 200 nodes) were compared under both random number generators. The results indicate that, generally, when quantum random number generators are used, faster convergence is achieved and better results are obtained.

This paper presents a methodology to design under-frequency load shedding (UFLS) schemes in power systems, which is supported by transient studies of critical contingencies related to primary load/generation imbalances that deplete the system primary reserve. The load shedding and frequency set points of each stage related to the UFLS scheme are determined based on the power system inertial and primary responses. The present methodology can be applied to power grids with both conventional and intermittent renewable generation power plants. To showcase the usefulness of the developed UFLS scheme, this was implemented in PSS/E Explore v35 and applied to the 39-bus New England test system including two renewable power plants.

The work describes the development of a three-level converter based on a switched source capable of generating typical fault signals present in the power grid. The process involves designing, simulating, and implementing the converter using an STM32 NUCLEO-F746ZG DSP board and an H-MOSFET bridge card acting as an inverter. The control circuit is implemented through Simulink, emulating various typical faults found in power grids. A VHDL-based data acquisition system using a GENESYS2 FPGA has also been developed to analyze and record the emulated fault signals. The generated faults include sag, swell, flicker, harmonics, and variations according to the IEEE 1159-2009 standard. The obtained results enable the evaluation of the impact of these signals on the converter and the electrical system, which is crucial for enhancing the quality and stability of the power supply and designing more reliable systems.

The Internet of Things (IoT) is a technological paradigm which has the capability to interconnect electronic devices capable of communicating machines to machines, machines to people and people to people. These devices are useful in industry to monitor the health of machines in production lines in order to avoid down-times. These down-times can cost a great deal of money. In this paper, we present the develop of an IoT System to detect promptly incipient failures of the machines which operate in lines production for the automotive industry. Particularly for the automotive industry which manufacture electrical harnesses. In this paper, we propose the ESP32 NodeMCu V2 module for the monitoring the health conditions for Komax Alpha 550 cutting machine. The IoT architecture allow to connect our IoT System with the Andon System.

Most biomedical research has focused on the study of ECG signals to diagnose heart or chronic diseases such as diabetes and hypertension, among others. Most heart-related diseases are identified by changes in the morphology of the waves and complexes that form the ECG signals, however, automated techniques for detecting ECG signal features result in expensive equipment. In this article, the authors present automatic feature detection and extraction techniques in real ECG signals using low-cost platforms and algorithms based on the discrete wavelet transform. The platform used to acquire ECG signals is the ADS1293EVM acquisition card and the MATLAB software is used to compute these signals. The developed program is evaluated with 30 ECG recordings of 6 seconds, having results in the detection of characteristics of interest greater than 90%.

The protein side chain packing is a well-known NP-hard computational problem and of prime relevance to the study of protein structure-function relationship and protein design. Central to the solution of this problem is the design of rotamer libraries. These libraries have improved with respect to the coverage of torsion angles, at the cost of becoming larger and more complex. However, larger libraries imply a larger size of the algorithm’s search space. To address this issue, we propose a method for designing rotamer libraries which considers pairs of consecutive amino acids, the resulting library of pairs leads to an exponential reduction of the size of the search space the algorithm needs to analyze. The proposed approach is based on the k-modes clustering algorithm to generate the library. The proposed method starts with a set of target protein structures and a desired coverage level of dihedral angles in a target dataset, then for each pair of consecutive residues in the target set, generates a set of pairs of rotamers that attains the pre-specified coverage level. We show that our proposed method generates good achievable accuracy when built on a set of 149 structures and tested over two sets of 65 and 373 protein structures.

Abstract—Cervical cancer is one of the leading causes of cancer-related deaths in women worldwide. Despite advances in early detection and treatment, it remains a significant public health issue, especially in countries with limited resources. Cervical cytology, such as the Pap smear, has been a fundamental tool in the early detection of cervical lesions and the prevention of cervical cancer. However, the accurate and reliable detection of abnormal regions in cytological samples can be challenging, especially in cases of low-quality and low-definition images. In the field of cervical cytology, precise detection of regions of interest is crucial for the early and effective diagnosis of cervical cancer. In this study, we propose the use of the Difference of Gaussians method to enhance blob detection in cytological samples. This method allows for the identification and delineation of areas of interest, even in images with poor quality and definition. By adjusting the values of the standard deviation, the detection sensitivity can be tailored to the specific characteristics of each sample. This promising methodology has the potential to significantly improve the diagnostic process and contribute to earlier and more accurate detection of cervical cancer.

This paper analyzes the dynamic contribution of doubly fed induction generator (DFIG) type wind turbine unit and energy storage systems (ESS), using super capacitors (CES) and superconducting coils (SMES) for frequency regulation services of the power system in a regulated environment. A generalized mathematical formulation is developed, which is capable of modeling systems in state space for an unlimited number of areas and devices per area from the parameters of the different devices in an automatic way. The control strategy used is realized through the implementation of a proportional-integral (PI) controller which, besides being simple to implement in the generalized mathematical formulation, has also proven to deliver satisfactory results for frequency control problems. The parameters of PI controllers are tuned to the optimum values using a multiobjective genetic algorithm (GA). The DFIG units considered in this work are presented by means of models with virtual inertia control schemes in order to support frequency regulation. Finally, the behavior of the IEEE 24-buses system under different operating conditions is analyzed, demonstrating that the coordinated action of the ESS/DFIG units in each operating area provides a transient benefit by arresting the initial system frequency dip as well as the power flow deviation on the transmission lines. In addition, the analysis demonstrates that the response of the ESS units is better and faster than that of the DFIG units in damping system oscillations.

The human hand is the main mechanical and sensory tool that allows us to interact with the environment. However, amputees face challenges in their daily lives. To address this, prosthetic hands have emerged as an effective solution. Surface electromyography (sEMG) signals are used to automate the control of these prostheses. Electromyographic (EMG) signals are recordings of the electrical activity generated in muscles during muscle contraction and relaxation. The processing and classification of EMG signals, applying statistical feature analysis, are of great importance in biomedical research and related areas. The aim of this study was to classify six EMG signals representing different hand movements by extracting statistical features in the time domain. The importance of key steps such as filtering, rectification and segmentation was emphasized to obtain an adequate representation of the data. Through feature extraction, the classification of hand opening and closing states was achieved. This highlights the need for careful feature selection in the classification process.

This paper adopts a power system stabilizer (PSS) con- trol structure-based design method to damp out inter-area modes in power systems through non-conventional equipment such as static Var compensator (SVC) and wind turbine generator (WTG). The PSS controller is designed using up to 2 lead/lag stages and is tuned by making stable all trajectories of the dominant modes. Compar- isons of different combinations of feedback signals and actuator control actions are exhibited achieving different damping levels. The correct operation of the controller is analyzed when both actua- tors are added to the system, thus the oscillatory mode is mitigated in case of large disturbances, adding up to 20% of damping. Small- signal analysis (SSA) and time domain simulations are provided to evaluate the effectiveness of the controller under disturbances

In this article, electricity demand forecasting is performed by Artificial Intelligence (AI), the technique used was an Artificial Neural Network (ANN). The ANN is created from 2 years of data (17520 hours of operation/supply) of the Mexican National Electric System (SEN), obtained from the system operator (National Energy Control Center, CENACE). In order to increase the accuracy of the network, an exhaustive filtering of atypical values of the raw data was performed. The developed forecasting method was applied to the region of Baja California Sur (BCS), a province of small demand compared to the other areas of the Mexican SEN. The 14-year electricity demand of the region was estimated using ANN. The results obtained are evaluated.

This article presents a didactic integration of the PowerLogic™ module into the laboratory at Universidad Polit´ecnica Salesiana (UPS). Research and analysis have been conducted to incorporate didactic methods that allow students to acquire academic knowledge and gain familiarity with the work environment, generating academic and industrial benefits. Additionally, equipment has been integrated, and research has been carried out on the IEC 61850 System Configurator® software communication protocol and Modbus TCP/IP.

Currently, electric power systems have been integrating massive amounts of renewable energy sources based on power electronics converters. This tendency has provoked new stability issues because of the interactions of controls in power electronics with passive elements in the power system. As a result, new oscillation issues have been reported, such as resonance problems in wind turbine generators (WTGs) with series compensation. This paper evaluates resonance problems in a WTG Type 4 (WTG4) using the impedance-based stability criterion due to phase-lock loop (PLL) control interactions. The sensitivity of the WTG4 to the bandwidth of the PLL is assessed using small-signal models and nonlinear time-domain simulations in Matlab/Simulink are conducted to support the results.

The implementation of renewable energies in electric power systems requires the integration of direct currentbased transmission technologies. In HVDC systems, half bridge modular multilevel converters (HB-MMC) are frequently used, however, a major challenge for DC-based power conversion and transmission systemsistheir protection againstshort-circuit faults on the DC side. Short-circuit fault currents have fast rise rates as well as high current amplitudes. Although the full-bridge topology (FB-MMC) is fault tolerant, it suffers from high switching losses. In this paper, the implementation of a hybrid HB/FB MMC with one path for the steady-state current and another path for the fault current is proposed. The fault current evaluation through the hybrid MMC is implemented in PSCAD/EMTDC.

This paper analyses the environmental impact of power generation plants in Mexico throughout their life cycle assessment (LCA) from the cradle to the grave. The studies were carried out in the SimaPro Software and using a functional unit of 1 kWh. The obtained results let to know which of the generation technologies used in Mexico is more polluting and at what stage of its life cycle, such as assembly, use, or disassembly, it pollutes more. With the obtained results, it is possible to know the optimal technologies to be installed and propose solutions to reduce the impact of the most polluting technologies. A global ranking with generation technologies was calculated. This ranking shows that the least polluting technology is nuclear power, followed by photovoltaic plants, while the most polluting technology is combined cycle plants.

This work proposes a renewable energy generation system that has a similar behavior has a synchronous generator in terms of reliability in the power supply and inertial response against power changes and grid voltage variations. The main contributions of this work are: (1) the variability absorption by means of a battery energy storage system, whose charge and discharge are determined by a variability calculation algorithm based in Kalman filter estimation, (2) the dynamical modeling of the virtual inertia-based inverter and its corresponding response to power variations and grid voltage disturbance, and (3) the modeling of a nonlinear optimal control for the inertia-based inverter system where the reactive power injection is regulated. Simulations results are presented in order to demonstrate the effectiveness of the proposed methodologies.

In an islanded microgrid, the grid-forming (GFM) converters are used to stablish an AC voltage whereas the grid-following (GFL) converters rely on the formed AC voltage to supply the loads. This paper presents a system consisting of a GFM converter with a battery pack and a GFL converter with a photovoltaic (PV) array. For this work, it is assumed that the microgrid is in islanded mode. The proposed control strategy allowed the GFM converter to form a stable AC voltage, regulate its DC voltage, and simultaneously charge or discharge the batteries without being affected by load changes. An outstanding capability for the GFM converter was proposed as part of the control strategy, which is carrying out the charging of the batteries during islanded mode while the AC voltage is being formed. This is suitable for the case when there is surplus energy in a microgrid, and the demand is less than the available energy. It also enabled the GFL converter to extract the required power from the PV array, for the loads and for the battery charging process. The proposed GFL converter control strategy is simple and easy to implement, avoiding the use of complex PV power extraction algorithms. The simulation tests demonstrated that the microgrid was not significantly affected by load changes nor by fluctuations of irradiance.

Spinning reserves of generating units are essential for the stability, continuity, and reliability of electrical power systems. However, the increasing integration of wind farms (WF) into power grids has led to a reduction in power reserves due to the displacement of conventional plants based on synchronous generators. Therefore, some grid codes require WF to provide this auxiliary service, which can be achieved by operating variable speed wind turbines (VSWT) in deloading mode. To this end, this paper presents an optimal power flow (OPF) model with WF complying with spinning reserves considering that VSWT use pitch angle control deloading. One where the WF distributes the responsibility of providing spinning reserves among VSWT according to their operating points. As a result, the specific blade pitch angle of each wind turbine contained in the WF is obtained. The present approach is applied to the IEEE 5-bus test system with an 80-MW wind farm comprising 40 turbines. The power system operation is compared with and without the WF, over a period of 12 hours with varying operating conditions, i.e., using variable patterns of system demand and wind speed.

The use of DC/DC converters with high gain has become indispensable in applications such as renewable energy, electric vehicles, DC microgrids and energy storage. Some requirements are high conversion ratios, resulting in high order converters. That is, converters with a greater number of reactive elements (inductors and capacitors) as well as switches. One of the challenges of these systems is the controller design, which must be able to regulate the output, with fast dynamics, mitigate the effects of parametric uncertainties and rejection to disturbances in the system inputs. The controller design requiere a controllability analysis to verify whether is state variable is adequate. This paper presents the controllability analysis based on switched linear systems of a quadratic Buck converter with reduced redundant power processing ($R^2P^2$) with an input LC filter. In addition, a comparison of energy processing based on graph theory against cascading structure is presented.

With the increasing integration of flexible generation technologies into modern electrical power systems, novel mechanisms and perspectives are essential to ensure system stability. Most flexible generation sources, such as renewable energy systems, are interconnected to the power grid via electronic devices. However, these devices lack inertia, which can lead to grid instability or even collapse during disturbances. In electrical power systems, maintaining stable frequency and voltage is critical. Conventional synchronous generators employ two controls, namely Load Frequency Control (LFC) and Automatic Voltage Regulator (AVR), to keep these parameters within a reliable range. This study focuses on analyzing the interaction between these two controls during system disturbances under low inertia conditions. To address the stability challenges, a virtual inertia control is proposed to provide support to the system. Furthermore, the study examines changes in voltage at the generator terminals following a disturbance, due to the reactive power demand, and evaluates its impact on the LFC control loop. By investigating the interplay of LFC and AVR controls in the presence of disturbances and low inertia conditions, this work contributes to a deeper understanding of maintaining stability in power systems with flexible generation. The findings shed light on the effectiveness of virtual inertia control in enhancing system stability and provide insights into managing voltage variations for improved control loop performance.

The rapid expansion of the Internet of Things (IoT), now with billions of mainly wirelessly interconnected devices, has brought concerns and challenges regarding the security of IoT networks and devices, as they are often vulnerable to attacks. In this paper, a measurement campaign will investigate the path loss experienced by a portable IoT hacking tool, known commercially as \textit{Flipper Zero}, when used in transmitter mode, in order to shed light on the maximum achievable distance at which an IoT device can still receive an eavesdropper's signal above a minimum power level. The path loss measurements are performed in three different outdoor environments. The results show that the hacking tool transmitted signal can reach up to 15 meters with power above -90 dBm, which is still in the sensitivity range of many IoT devices, thus revealing potential vulnerabilities and security risks in real-world scenarios.

This article deals with the problem of harmonic resonance in power systems, which causes harmonic amplification and can worsen existing harmonic problems in the system. Therefore, performing a frequency response analysis is essential to identify series and parallel resonances in the system within a frequency range of interest. To address this issue, it is proposed to use a Frequency-Dependent Network Equivalent (FDNE) based on a rational function in the z-domain. This approach is based on acquiring measurement data and implementing parameter identification techniques using the recursive least squares method to evaluate the frequency response. To validate the proposed method, its implementation is carried out in different case studies.

This paper presents two Neural Networks (NN) that estimate the wind speed and optimal mechanical speed. These use the Tip Speed Ratio (TSR) technique for Maximum Power Point Tracking (MPPT) in a simulated Wind Energy Conversion System (WECS) connected to the grid. The purpose of the NNs estimators is to provide the appropriate mechanical speed reference on the speed control loop despite the random behavior of the wind, avoiding the use of an anemometer from the cut-in speed. This is achieved by estimating the wind speed from the power of the wind turbine (WT) and mechanical speed of the permanent magnet synchronous machine (PMSM). The performance of the control applied to the converters is evaluated through a simulation in MATLAB/Simulink. The stability of the machine-side converter (MSC) and the grid-side converter (GSC) systems is demonstrated by applying a stepped wind speed profile and a random wind speed profile.

This work addresses the observer design for linear hybrid systems, which are eventually observable when some linear system does not fulfill the typical observability propositions. Nevertheless, it is possible to establish an observer design. The present article proposes a method for detecting the real eigenvalues contained in the output of each linear system. In the proposed method, each linear system is decomposed into a Jordan form, then entered into an algorithm that determines the real eigenvalues of the measured output.

The three-phase rectifiers are widely used in different industrial applications, like energy storage systems, uninterruptible power supplies, renewable energy generation systems due to their high-power transfer and its power factor correction capability with high-quality DC output and sinusoidal input currents. To control the power factor, the model predictive control technique has emerged as a suitable and intuitive control strategy, however, the high computational cost limits the converter frequency operation. As a solution, in this paper an artificial neural network based on model predictive control is proposed to obtain a three-phase sinusoidal input currents under different power demands, reducing the computational cost.

The main objective of this work is to show how the empirical equation was obtained that quantifies the amount of heat that the cooling unit with mobile elements removes from the drilling fluid (which is the main objective of this device), the amount of heat that is transferred by evaporation of the water contained in the drilling fluid and that mixes with the air, the amount of sensible heat that is transmitted to the air and the amount of heat that is transmitted by convection to the environment. In order to quantify the amount of heat that is transferred by convection to the environment, it was necessary to develop an appropriate equation for the Nusselt number (Nu) as a function of Reynolds (Re) and Prandtl (Pr).

This work presents the design proposal for a pseudorandom number generator with two Linear Feedback Shift Register LFSR. The operating frequency of one of the registers depends on the frequency of a neuronal module, that is capable of producing spiking of bursting phasic and bursting tonic form, which, for each pulse generated for the neuronal module, produces an LFSR displacement and a new output data. The data obtained from the pseudorandom number generator with the neuronal module was printed in an archive text TXT to process and analyze the sequence by the linear complexity test proposed by the National Institute of Standards and Technology NIST and validate that the sequence is complex enough and aleatory. Also, the circuit implementation was carried out in the FPGA Field Programmable Gate Array Virtex 7 xc7vx485t-2ffg1761 device.

In this paper the approximate mechanical torque method is developed for the study of a wind farm. The response of that method is compared with the response of a detailed wind farm that consist of four variable speed wind turbines with PMSG technology. The detailed description of the involved models for the aggregation method allows easy implementation on different platforms, such as open source simulators. For comparison purposes, in the aggregated and detailed models, a simulation case study considering variable incident wind speed in each turbine was developed. As a result, time responses demonstrate the functionality of the aggregated model, as well as its easy implementation in a commercial simulation software, and their simulation in the open source Python software.

In this paper, the design of a control strategy for DC battery charger employing a high-gain boost converter is presented. The electronic converter topology used for battery charging is composed of two inductors activated by two power switches. Hence, the power converter achieves a high-gain output voltage compared with the traditional boost converter. The control scheme proposed is based on the charging profile constant current-constant voltage; thus, the control law is divided into two feedback loops. During the constant current stage, a proportional-integral control law provides adequate current tracking injection, while the battery voltage increases to reach a desired reference value. During the constant voltage stage, the control objective is to guarantee a constant output voltage; meanwhile, the battery current decreases to a minimum value. Finally, in order to evaluate the proposed control scheme performance, numerical results are presented.

Sea lions (Zalophus Wollebaeki) are a protected species, and effective monitoring is crucial for habitat preservation and behavioral studies. However, manual sea lion counting is laborious and error-prone. In this paper, we explore the use of two standard convolutional neural network models (YOLO-NAS and YOLOv8) for sea lion detection as a preliminary step toward automating the counting process. For this purpose, a dataset of images and videos of sea lions was collected in their natural environment in the Galapagos Islands. The results demonstrate that both models exhibit promising detection capabilities, successfully identifying almost all sea lions in the images. In particular, YOLOv8 shows to be more reliable in the detection of sea lions under challenging and complex conditions, while YOLO-NAS excels in the identification of a larger number of individuals, including those of a smaller size. These findings pave the way for future developments in automated sea lion counting tools, streamlining conservation efforts and advancing our understanding of this protected species.

Ground leakage currents can occur in transformerless grid-connected photovoltaic inverter systems, posing safety and performance issues. This paper provides a brief overview of recent research efforts focused on reducing or eliminating ground leakage currents in transformerless grid-connected single-phase voltage source inverters. The main objective of this study is to examine and describe the various methods used, including modifications or proposals for topologies, switching schemes, and filters. The paper aims at offering insights into the progress made to address the problem of ground leakage currents and their mitigation strategies in transformerless grid-connected PV inverters.

Single-phase non-isolated photovoltaic inverters are widely used to inject electrical power to the mains. In these systems, the leakage ground current is one of the more important issues to be solved. Several topologies have been proposed and studied during last decade contributing to reduce size, improve the efficiency and presenting solutions to the leakage ground current problem. In this paper, a five-level single-phase inverter topology is proposed. It consists in five power controlled semiconductors and five diodes. The converter operation is derive and a pulse width modulation strategy is particularly proposed. An study regarding efficiency and power losses distribution is also performed. The proposed single-phase transformerless multilevel inverter is validated by means of numerical simulations and the results and characteristics are compared with existing solutions.

Currently, induction motor (IM) is the most common electric machine in the industry. IMs are robust machines; however, they are prone to suffer failures, often located in stator winding due to external factors such as electrical or mechanical overstress, thermal changes, and environmental conditions, which affect the proper functioning of the IM. Therefore, implementing a monitoring system serves the purpose of timely detecting stator failures and determining their severity in order to evaluate the physical state of the winding. In this work, a machine learning strategy is proposed for the analysis and recognition of various degrees of severity of short-circuit damages in the winding of an IM. In general, this strategy consists of statistical indicators, the one-way analysis of variance (ANOVA) method, and a support vector machine (SVM) classifier. Results demonstrate the effectiveness of the proposal.

This paper presents a study of the proceedings done towards the electrical commissioning of an 83 kW laboratory, all- electric sport motorbike to obtain energetic analysis and thermal experimental results as a social catalyst for electrification transportation. The electric motorbike mainly uses an 83 kW, Permanent Magnet Synchronous Motor (PMSM) driven by a 350 V, 110 kW motor controller powered by a 350 V, 5.6 kWh LiPo battery pack. The electric train system of the motorbike is controlled and operated by a microcontroller with a touchscreen display where several parameters are monitored, such as velocity, temperature, battery SOC among other indexes. A description of the tests done with the motorbike prototype rig is summarized along the paper in different steady-state operating points, such that electrical and thermal results obtained from the system are analyzed verifying the feasibility of the prototype for personal transportation or racing applications.

A statistical analysis method is developed for estimating patterns of behavior and their amplification from the observed system response to random variations. Spectral proper orthogonal decomposition (SPOD) is used to examine gains and phase relationships between signals from simulated data. Based on frequency domain and Fourier analysis, the SPOD is employed to decompose forced oscillations into a set of space-time orthogonal modes. Such an approach improves the ability of Fourier analysis to study stochastically forced oscillations in complex power system models. Analytical criteria to describe the energy relationships in the observed oscillations are derived, and a physical interpretation of the stochastic modes is suggested. The methodology is illustrated on simulated random oscillations of a realistic test power system.

An important stage in recognizing Land Cover (LC) on satellite images is the determination of a set of features that can best describe it and help infer between types of covers. This article analyzes and selects texture features of the Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP). The selection of texture features makes it possible to reduce the dimensionality to a relevant subset that provides information in improves the identification between types of LC. This in order to improve performance in semantic segmentation methods in satellite images. The images used for the analysis experiments corresponded to multispectral satellite images from the Sentinel 2 satellite of COPERNICUS of the European Space Agency (ESA). GLCM and LBP features are extracted from patches of different LCs of interest (agricultural vegetation, vegetation, soil, water, and urban). In addition, coefficients of variation of values of the features are obtained, and an analysis of the variation behavior between the different interest coverage and within the same type of LCs is carried out. Finally, relevant features were selected considering the behavior of the coefficient of variation and those obtained with the Relieff method.

In this article, reducing the mutual coupling between the elements of a 2x1 compact UWB-MIMO antenna is achieved by applying the defected ground structure (DGS) technique. The DGS consists of two inverted L-shaped slots and one Ishaped notch. The two multiple-input multiple-output (MIMO) antenna elements are rectangular patch antennas with a Tshaped microstrip attached to one end. The dielectric substrate of the antenna is FR4 with a relative permittivity of 4.4 and a thickness of 1.6 mm. The separation between the centers of the two elements has been achieved to be only 0.027 the operating wavelength, and the MIMO antenna has a dimension of 30x25 mm. Simulated and experimental performance results are presented. Certain geometric dimensions of the DGS are optimized in order to achieve suppression of narrow bands in the frequency bands of WiMAX and WLAN technologies, from 3 to 3.6 GHz and from 5.3 to 6.3 GHz, respectively. The proposed antenna has a bandwidth of 3.6 GHz to greater than 12 GHz and an estimated gain of up to 3.8 dB.

This paper proposes a model predictive control (MPC) algorithm optimized using the artificial bee colony algorithm (ABC) to control the temperature of the pasteurization product in a small-scale pasteurization plant following a set point trajectory and minimizing power consumption. The proposed algorithm is compared with the MPC tuned by trial and error. The results show that the proposed ABC optimization-based MPC algorithm shows improvements in relation to trajectory tracking and disturbance rejection. A significant reduction in the integral squared error (ISE) of approximately 68.12% and in the settle times (up and down) of 58.90% and 84.40%, respectively, was achieved.

Currently, novel approaches are being developed to overcome the imminent Moore's law failure. The techniques attempt to gain greater computing power by reducing the number of transistors. They are based on nonlinear or chaos computing. Nonlinear dynamics is an essential source of various patterns that can represent natural systems or perform computational tasks. This work presents the implementation of a voltage logic gate based on the plane equation. This is achieved by using two variables of the plane equation as inputs and the other as output.

This paper presents a passivity-based control of a non-inverting buck-boost converter coupled to a proton exchange membrane fuel cell. The controller primary objective is to maintain a stable load voltage despite fuel cell voltage variations by assuring precise current control. To accomplish this, two feedback loops are used: an outer loop that generates the current reference based on the output voltage using a proportional-integral (PI) action, and an inner loop that tracks the current using the passive characteristics of the system. To improve the reliability of the inner current loop, an immersion-invariant (I\&I) load estimator is developed. The overall efficacy of the controller is determined by numerical simulations that demonstrate its capacity to manage load variations and maintain a stable, regulated DC output voltage despite voltage changes from the fuel-cell stack.

The integration of new sources of electricity generation to the General Distribution Networks (RGD), have meant topological changes in the system, such as the change of direction of current flows, resulting in bidirectional flows, likewise, requirements have been set that must be met when distributed generation units (DG) are connected to distribution systems, one of them is the ability for the unit (DG) to remain connected when a fault occurs. The traditional protection schemes that are operating in radial distribution networks tend not to operate correctly with the presence of (GD) units, since they fail to detect or isolate the fault or may act outside their area of operation. For this reason, it is important to make changes in the protection schemes, which is why a more selective protection is necessary. This paper proposes a protection scheme capable of identifying the magnitude of the fault by means of directional relays in the presence of distributed generation that is designed in the ATP/EMTP platform.

Monitoring disturbances in power quality (PQD) has gained importance in recent decades. This interest arises from the fact that disturbances associated with power quality directly affect the equipment connected to the grid, leading to malfunctions or complete equipment failure. For this reason, efforts have been made to optimize the methods for detecting these disturbances occurring in the electrical network. In this study, a Python algorithm is proposed to improve the detection and classification of seven types of simple electrical disturbances (\emph{sag, swell, flicker, oscillatory transient, interruption, harmonics, and notch}). The algorithm varies the combination of characteristic vectors extracted (\emph{energy, mean, standard deviation, skewness, Shannon entropy, RMS, kurtosis, and log-Energy entropy}) obtained from the Discrete Wavelet Transform (DWT) through Multiresolution Analysis (MRA) with 6 levels of detail. A database was generated by sampling four thousand electrical disturbances at 10 \emph{kHz}. The experiment involved connecting a \emph{Beagle Bone Black} (BBB) development board to a \emph{BK Precision 4064} arbitrary waveform generator. Combinations of two characteristic vectors were extracted from each signal in the database to evaluate them using the \emph{Random Forest} classifier and determine which one is most suitable for this type of analysis based on their accuracy percentages.

Point cloud object classification has many challenges related to the quality of the data used to perform the task. The quality of the data is related to the sensor calibration, data acquisition methodology and noise or perturbances on the measurement process. It is required for real time practical applications a solution that performs well even with medium quality data. In this paper, it is presented the design and implementation of a geometric convolutional system that works using the mathematical framework of conformal algebra to reduce the impact of having object point clouds that are incomplete, occluded, or noisy, which causes deformations on the objects to classify. We accomplish this by designing three preprocessing layers in our convolutional system, that use conformal geometric space to perform transformations of the input cloud point: a mapping from 3D to conformal space, a search for the best object perspective and an adjustment of the distance between sampling planes that describe the object.

The automatic evaluation of mental state/emotions during driving constitutes a powerful tool for assessing the risk of accidents. It is known that about 97% of accidents in Mexico during 2021 were related to human causes, while 95% of such causes were attributable to drivers. In this work, we use encephalographic and cardiographic tools to asses the emotions and the state of mind of drivers during a virtual tour of city driving conditions. Two different traffic and pedestrian crossing conditions were used for the same city path. Our results arouse that significant differences between low- and high-traffic conditions are present in drivers dependent mainly on driver experience. Experienced drivers, irrespective of their gender, are considerably more robust to face the variations in traffic measured in both their biometric data and vehicle dynamics performance. The results of this work may help assess the feasibility of deriving customizable driving assistance systems that take into account the emotions of driving.

Increasing penetration of behind-the-meter (BTM) resources in distribution systems is a prominent factor for inducing the death spiral in retail markets. Owing to realtime deviations in BTM generation, the energy procured by retailers in the day-ahead markets may no longer be sufficient. Thus, the retailer must procure excess energy at spot prices to compensate these deviations, which in turn increases energy costs for the customers, driving more BTM resource adoption and reinforcing the death spiral. In this context, this paper develops a framework for the electric retailer to procure energy-storage-as-a-service (ESaaS) to mitigate the operational uncertainty. Volume firming contracts are established between the retailer and utility-scale energy storage operators to minimize the retailer’s energy procurement costs in spot markets, thereby limiting energy costs for the customers in spite of high uncertainty introduced by BTM resources. This paper develops the mathematical framework for the volume firming contracts, obtains conditions for optimality and profitability of the contracts, and discusses implications on social welfare. Simulation results verify the effectiveness of the developed framework in improving the financial situation of the retailers and ESaaS providers under uncertain generation conditions of residential BTM resources.

Short circuits in windings is a major factor contributing to the damage observed in electrical transformers, therefore, early detection during the initial stages is of vital importance to prevent more extensive damage. This paper proposes an approach for detecting short circuits through vibration analysis. The proposed methodology enables the analysis of various conditions, ranging from a healthy state to six levels of short-circuit turns in an unloaded transformer. To accomplish this detection, it is employed a combination of frequency domain features extraction after the application of the FFT, principal component analysis, and a classification technique such as K-nearest neighbors or Support Vector Machine. The proposed approach accurately determines the extent of damage present in the windings. The results show the effectiveness of this proposal in precisely identifying the severity of damage in the transformer.

One of the great challenges of HVDC systems is the protection against short-circuit faults. The propagation of the fault effects in DC systems is faster than in AC networks mainly because to the low resistance, so a new breed of detection algorithms is required, much faster and more effective, with operation times to 10 milliseconds. This work proposes a step-forward scheme to detect and classify high and low resistance pole-to-pole and pole-to-ground short circuit faults in a MMC-HVDC system by combining the use of Voltage Controlled Oscillator (VCO) modules with a smoothing reactor. Case studies to verify the effectiveness of the proposed strategy are performed in PSCAD/EMTDC with a point-to-point HVDC system.

Intensity and color of the Light are essential factors for the proper growth of a crop. Currently, artificial lighting is used to grow indoors where there is no access to sunlight. In this work, the implementation of a novel and simple automated lighting system for an indoor growing prototype is presented: This system provides independently blue and red LED lighting with a light intensity of 130 PPFD for the growth of Lettuce "Baby Sucrine" variety. The prototype uses a PSO anti-windup PID controller to ensure that the light intensity remains constant. The color signal references to be applied into the crop are given in base of agricultural research in this topic. The light intensity is monitored through a web interface as well as the color reference. The interface displays lighting data for the last 24 hours. Results obtained from lettuce growing in the prototype are compared with a control crop grown under 130 PPFD white LED lighting with the same photoperiods.

Nowadays, internet access and the sophisticated processing capacities of electronic devices for the capture and visualization of visual information, such as images; have allowed young users practices such as downloading, storing, editing, and re-sharing of images without authorization of the image owner. To solve this problem a watermarking algorithm for digital images is proposed, which is able to warranty the image protection against the most common editions incorporated into the capture devices and artistic editions integrated into the upload option of the social media. The experimental results show a high performance of the proposed algorithm, due to, a minimum edition of the protected image exhibiting to the naked eye the watermark pattern, by another way, the average performance is reported with a PSNR=42dB and NCD=0.0885.