ABSTRACT. In this paper we presents the convex Takagi-Sugeno modelling methodology for nonlinear systems, through examples, the effectiveness of the nonlinear systems modeling technique in defined operating ranges is demonstrated. It emphasizes the importance of using the Takagi-Sugeno technique to model nonlinear systems, with a detailed explanation of the complexity involved in implementing this technique. Additionally, It can be observed that the convex Takagi-Sugeno model exactly follows the behavior of the nonlinear system.

ABSTRACT. This work presents the design of a Recurrent High- Order Neural Network (RHONN) capable of identifying systems with unknown parameters and robustness against disturbances, specifically for a permanent magnet DC motor. The RHONN is trained using the Extended Kalman Filter (EKF), an efficient and robust training technique that enables online training and facilitates the design of an equivalent system and the application of PID control to modify the motor’s angular velocity. This demonstrates the remarkable versatility of neural networks to tolerate parametric uncertainties, disturbances, and adapt in realtime to an unknown system, utilizing data acquired through the system’s sensors. Furthermore, it is shown that a RHONN trained by the EKF possesses filtering capabilities and high efficiency, making it valuable for practical applications in systems that are partially equivalent to a classical model, with unknown parameters or noise in their data.

ABSTRACT. In this paper, we present the design of a sliding mode observer for a linear system, which represents a passive filter scheme intended to mitigate harmonic currents. The primary objective is to detect possible faults in the filter that may involve changes in the nominal values of its components. To calculate the necessary gains for the proper functioning of the observer, we employ the interiorpoint method in solving Linear Matrix Inequalities (LMIs). This process includes a proposal to effectively bound the gains. The main contribution of this work is the design of the sliding mode observer and its implementation in the mathematical model that represents the dynamics of the contaminated electrical grid and the passive filter. The presented methodology demonstrates the feasibility and efficiency of the observer for fault detection in passive filtering systems, thereby contributing to the improvement in the management and maintenance of electric power quality.

ABSTRACT. In this paper, we use topological data analysis for fault diagnosis in a nonlinear system. The studied system is a liquid-level structure conformed by two interconnected tanks, the liquid flow is regulated by solenoid valves, and the liquid level is measured with ultrasonic sensors. The developed strategy only depends on the sensor readings and does not require a mathematical model of the system. The software for testing the fault diagnosis algorithm was developed in Python and Matlab.

ABSTRACT. The high energy demand, which is mostly know by the use of fossil fuels, represents a serious problem due to the emission of greenhouse gases. An alternative solution to meet the growing energy demand is the production of green hydrogen from renewable energy sources. In a such sense, this work aims to optimize hydrogen production in a Proton Exchange Membrane (PEM) electrolyzer and study how to increase its efficiency by evaluating the behavior of a polarization curve through a parametric analysis of an electrolyzer considering variations in temperature and ambient pressure corresponding to several representative cities in the distribution systems of the electricity services company (in Mexico, Comision Federal de Electricidad, CFE). The study shows that any city with an average annual ambient temperature close to or equal to 40 degrees Celsius is optimal for the development and implementation of this technology. Technically, it is determined that it is possible to decrease the input voltage while increasing the current flow, representing more favorable conditions for the development of this technology.

ABSTRACT. Mexico is rapidly transitioning from thermal power plants to renewable energy sources, with photovoltaic capacity increasing by 179.23% between 2019 and 2022. This shift presents technical challenges, particularly the loss of inertia crucial for grid stability. This study explores Grid-Forming (GFM) inverters as innovative solutions compared to Grid-Following (GFL) types. GFM inverters independently generate voltage signals, providing virtual inertia that enhances grid stability. The paper compares Droop Control, Power Synchronization Control (PSC), and Virtual Synchronous Machine (VISMA) control systems, focusing on a single 33 kV bus case study under varying loads. Results highlight that GFM inverters, especially those with VISMA control, improve dynamic response by reducing the rate of change of frequency (ROCOF) and enhancing frequency stability, pivotal for integrating renewable energy into Mexico's electrical grid.

ABSTRACT. This paper addresses the growing interest in microgrid (MG) research, driven by the integration of renewable energy sources and the need for enhanced grid resilience and flexibility. The implementation of efficient and stable control systems is crucial for the optimization of MG operation. This study investigates the resilience of the consensus algorithm within an MG comprising three-phase four-wire generators under a distributed control system. The simulations explore various communication scenarios to assess the tolerance of the communication link under failures and their impact on MG stability. The results highlight that consensus algorithm maintain robustness and resilience even when communication links fail. These characteristics are essential for distributed control systems, where communication between generators can be interrupted or exposes to temporary failures. Consequently, effective power sharing in the MG can still be achieved, underscoring the efficacy of the algorithm in maintaining MG performance despite communication challenges.

ABSTRACT. This paper presents a control strategy for the gridfollowing inverter (GFL), which improves performance and ensures the operation of the microgrid in an event of a fault in the grid-forming inverter (GFR). The model of the converters used in a MG is shown. Control schemes are proposed for both GFR and GFL. Simulation results demonstrated good performance of GFL in reconfiguring itself upon GFR failure, successfully replacing it to keep the MG operational. Also, when the GFR is reintegrated, the GFL reconfigures again to act as a feeder. This is possible without the MG ceasing operation, maintaining AC voltage without disturbances during load changes.

ABSTRACT. Photovoltaic energy generation, although it offers a range of advantages, can cause overvoltages in the network and, consequently, cause damage to the equipment of consumers connected to the affected network and the distributor. This work considers the study of the voltage profile and operational loss of the ConGrid residential microgrid under different scenarios. To carry out the proposed study, the distribution network and conventional and distributed generation sources of the microgrid are modeled using the OpenDSS software. Furthermore, simulation results considering islanded and grid-connected operating scenarios are presented and discussed regarding their voltage and loss profiles.

ABSTRACT. This work precisely incorporates magnetic nonlinearity into the state-space model of the three-phase induction motor using piecewise conversion. This approach transforms RMS values from no-load testing into peak values of flux linkage and excitation current providing a computationally efficient alternative to the finite element method (FEM). The study presents a comparison between the results of the nonlinear statespace model and FEM, demonstrating that saturation effects, implemented in MATLAB/Simulink via linear interpolation and extrapolation, achieve a high level of accuracy. The maximum error observed in the V − I curves for the no-load test was 3.049% for the 10 HP induction motor analyzed, showcasing the effectiveness of this method, making suitable for incorporation model, algorihms, and control strategies using power electronic.

ABSTRACT. In this paper, the time-domain harmonic state estimation (TDHSE) for unbalanced three-phase power systems with non-linear loads is addressed. Considering the diversity and complexity of the nonlinear loads connected to the system, it is necessary to accurately estimate the distorted voltage and current waveforms at unmonitored busbars, particularly the dynamics at the point of common coupling. To address this problem, the state estimator is implemented through the Kalman filter algorithm associated with the nonlinear load identification method. This approach facilitates the evaluation of the dynamic behavior of electrical loads without requiring the formulation of a detailed mathematical model, considering the diversity of nonlinear loads connected to the power system. This combined methodology allows obtaining the state variables of the power system as the inputs to the system. In addition, an underdetermined measurement model is used, which allows achieving complete system observability. The harmonic contents are determined by applying the fast Fourier transform to the waveforms. The PSCAD/EMTDC® response is used to validate the TDHSE state estimator results, obtaining an adequate agreement.

ABSTRACT. In recent years, the increasing use of Unmanned Aerial Vehicles (UAV) has expanded into various fields due to the wide-ranging benefits they offer. Currently, most UAVs are equipped with propellers that function as actuators to enable the vehicles' flight. It is crucial to have a monitoring system that provides insights into the structural condition of these actuators, as a failure in them could result in a catastrophe for the vehicle.

ABSTRACT. Frequency control is one of the main objectives in the operation of the national interconnected system and when generation disconnection events, contingencies in electrical network equipment or disconnection of large blocks of load occur, the frequency can deviate up to action thresholds of protections or risk of cascade tripping of power system equipment. To analyze and characterize frequency stability, this paper presents a methodology for the assessment of frequency stability with performance indicators calculated from synchronized phasor measurements. The calculated indicators are the nadir frequency, rate of change of frequency (RoCoF) and frequency establishment time. The methodology is implemented in the analysis of frequency events of the Colombian interconnected system using time series data of synchrophasor measurements. The results showed the capacity of the proposed methodology and tool to characterize frequency events and the relationships between the different indicators calculated for each of the events.

ABSTRACT. Recent advancements in Artificial Intelligence (AI), particularly in the field of Natural Language Processing (NLP) and Large Language Models (LLMs), have opened up new avenues for processing qualitative data. This development is particularly interesting for the power system sector as it has an enormous amount of untapped qualitative data. Traditionally, decision-making in this sector have predominantly relied on quantitative data. However, qualitative data such as textual information hold untapped potential. These data can provide valuable insights and act as decision support tools in various power system tasks, including forecasting. The challenge however lies in effectively processing these qualitative data for specific tasks, such as power system forecasting. In this paper, we propose an approach to address this challenge. We perform sentiment analysis using LLMs on system operator comments from the Alberta Interconnected Electric System. The underlying idea is that these sentiments can reflect the state or ‘emotions’ of the power system network at any given moment and serve as useful signals for carrying out forecasting. We transform these sentiments into time series data and incorporate them as an additional feature for training a transmission loss forecasting model. Notably, we demonstrate that integrating NLP into transmission line loss forecasting is of value. We carried out experiments using three model classes, statistical, machine learning and deep learning. Depending on the forecasting model class and experiment type, we observe a forecast accuracy improvement of 2-3 percent across board. Overall, this research extends traditional forecasting methodologies by harnessing the power of NLP. We showcase its potential to enhance models and its practical relevance in power systems.

ABSTRACT. This paper uses the HACA clustering method to design electromechanical oscillation control using LQG control applied to generator exciters and also applied to a SVC in a power system.The system is identified by the ERA algorithm and the LQG control is then tuned and added to the exciters or the SVC. As a result, a damping of up to 7\% is obtained in the inter-area mode of a test system.

ABSTRACT. This paper proposes an alternative way of designing multiple power system stabilizers (PSSs) for damping electromechanical modes. The approach is based on a system identification method that allows the computation of the first-order sensitivities of the eigenvalues of power system linear models. Thus, the eigensystem realization algorithm (ERA) is used to identify the power system linear model. This proposal derives a state-space representation using power system measurements to compute the power system eigenvalues and their residues for designing a single PSS for damping one mode or coordinated multiple PSSs for damping several modes. The attained results in two benchmark systems, Kundur and the New England transmission system-New York power system (NETS-NYPS), demonstrated the effectiveness of the proposed strategy in improving the dynamic response and power system stability for damping one or several electromechanical modes in large power systems, reaching stable behaviors in less than 2s and 7s for the Kundur and NETS-NYPS grids, respectively.

ABSTRACT. As the adoption of renewable energy sources grows, electric distribution companies face the challenge of efficiently managing electrical consumption. Low frequency, non-intrusive disaggregation using standard smart meters emerges as a promising tool to monitor and analyze consumption patterns. This enables companies to offer incentives, such as discounted rates, to users with efficient consumption behaviors. While several works have addressed the problem of non-intrusive load disaggregation from data sampled at high frequency, there is an important scarcity of tools and analysis in the context of low frequency applications. The latter is a critical limitation for the adoption of data driven strategies, since a large proportion of scalable and affordable meters sample at low frequency rates. The present study addresses this important limitation and focuses on the disaggregation of appliances from an aggregated consumption curve sampled at very low frequency data ($10^{-3}-10^{-2}$Hz). In particular, we analyze two critical high-power appliances: water heaters and electric vehicles. In addition to benchmarking and adapting existing disaggregation methodologies to the low frequency sampling domain. A novel real-world dataset released as part of this publication is accessible at: \href{https://drive.google.com/drive/folders/1JeF6dgKkba6ymyAmnovrwQ7huXebonfh}{this link}\footnote{Due to double-blind review, a Google Drive link is provided. In the final version, a download link form on an institutional web-page will be included.}.

ABSTRACT. The present article describes the current condition of the Mulegé interconnected system (SIM), located in the state of Baja California Sur, Mexico. A methodology is developed based on energy generation curtailment techniques to define the maximum integration of renewable generation, using 10% of undelivered solar energy as a basis. The operational reserve requirements established by the North American Electric Reliability Corporation (NERC) standards and the Grid Code issued by the Mexican Energy Regulatory Commission (CRE) are considered, as well as transmission constraints and reliability units (Must Run). Finally, the proposed renewable generation integration model is validated through dynamic simulations, considering frequency stability.

ABSTRACT. This work presents the design and configuration of the protection schemes of an electrical substation based on the IEC61850 standard to carry out measurement and communication between the protection devices. The implementation of IEC61850 for communication between devices and equipment in substations is carried out with the PCM600 software. The setting parameters of the protection schemes are entered through the DIGSI 5 V9.7 software. The evaluation of the performance of the protection devices is carried out through the “digital twin” of the Siemens brand to evaluate the operation or non-operation of the protection schemes in the event of a fault condition in some element of the electrical substation

ABSTRACT. In recent years, the world's carbon footprint has opened the way for the development of new clean energy generation systems. Among the most important of these is wind power generation, since this type of renewable generation takes full advantage of the natural resource that will be converted into electrical energy, as in the case of offshore wind farms. For this point, the new challenge is based on the transmission of the energy generated in remote areas. It is then that the implementation of a High Voltage Direct Current transmission system with a Modular Multilevel Converter comes into play in order to transport the energy generated from offshore wind, and that this is harnessed and transported to the point of consumption. These systems are not insulated from variations in grid parameters or even from the effects of external environmental factors. Despite being a new technology, widely studied, the design of controls for fault protection is still under development. In the present work, the affectation of the short-circuit current generated during two types of faults in the direct current line of the HVDC system is analyzed. The behavior of the Pole-To-Pole and Pole-To-Ground fault current in the presence of the DFIG machine was analyzed, with the objective of opening the panorama for the development of protection and fault mitigation schemes adequate to the integration of both systems.

ABSTRACT. This paper presents a new approach to select and coordinate short-term stability criteria for assessing, simultaneously, unstable oscillations, and transient, voltage, frequency instabilities in time-domain simulations. This is necessary because, currently, frequency instability has become a problem of great concern and, including its assessment in time-domain simulations along with all the other stability problems, must be performed carefully, to allow a correct stability assessment. As presented in the results of the paper, an incorrect selection of a stability criteria could lead to a failure in determining the specific problem initiating the instability, and thus to an inadequate selection of the control actions to stabilize the system.

ABSTRACT. Effective communication is crucial for mutual understanding and remains a significant challenge for Deaf and Hard-of-Hearing individuals. This study aims to establish the basis for creating a portable communication system that can translate Mexican Sign Language (MLS) to facilitate two-way communication for Deaf and Hard-of-Hearing individuals. The device achieves a high accuracy of 98% in recognizing individual signs and features real-time audio-to-text translation capabilities. The Random Forest algorithm demonstrated a recognition accuracy of 98.97% for each letter of the Mexican alphabet. The device maintained a confidence level of 98% under optimal lighting conditions but decreased to 83% in low-light environments. It successfully recognized signs at distances up to 100 cm, with notable performance in distinguishing visually similar signs. This study has established a foundational framework for creating a portable device that allows Deaf and Hard-of-Hearing individuals to communicate effectively. It proves the device's potential as a reliable tool for bidirectional communication and instills optimism about its potential impact.

ABSTRACT. The development of a classifier based on the extraction of HOG (Histogram of Oriented Gradients) type features and the SVM (Support Vector Machine) algorithm for the analysis of ocular regions of people belonging to different life stages is presented. The prediction of membership is within two groups of people youth and adulthood. A database of images (24x24 pixels) of 21 persons is used for training and evaluation of the classifier. We work with the HOG characteristic matrices considering 8 gradient orientations with cells of 8x8 pixels and blocks of 2x2 cells in each respective eye. The proposed technique to identify the classification of the person’s life stage with respect to the eye study offers an accuracy and recall of 0.94, as well as an F1-score value of 0.95, which indicates its efficiency in using fast processing techniques. Such a classifier can be used for medical purposes such as diagnosis and prediction, as well as within biometric or surveillance systems.

ABSTRACT. Logistic regression for functional data is a statistical technique used to model the relationship between functional predictor variables and a binary or multi-class outcome variable. The basic structure of the model remains the same as in conventional logistic regression, where the aim is to model the probability of an event occurring based on the input functions. Logistic regression for functional data allows you to capture complex patterns in the data that would not be accessible with traditional linear models. Logistic regression for functional data is a powerful tool for predictive and descriptive analyzes in a wide range of disciplines. However, its effective application requires a deep understanding of the functional nature of the data and the associated statistical techniques. That is why, based on what was mentioned above, in this work a detailed analysis is carried out on how to apply methodologies related to polynomials, Fourier series and splines to functional data classification problems through logistic regression; Subsequently, these methodologies are applied to two classification problems with functional data and finally a comparison is made between them and some classic methodologies.

ABSTRACT. Morphology is a descriptor of sedimentary rocks useful in earth science and economic geology. In the contour of the rock, there is information on its origin, transport, and deposition. The two most important parameters are general form and roundness. These parameters are obtained by algorithms that process images of outcrops or samples of them. Capturing these images is a difficult task due to inaccessibility or risk. On the other hand, to evaluate an algorithm, it is necessary to capture several phenomena since usually, a single phenomenon produces only one type of contour. In this work, we train a Wasserstein Generative Adversarial Network (WGAN) that synthesizes contour images of rocks with a wide range of morphology. The WGAN was trained with 5200 contour images of various phenomena such as ash and debris flows, lahars, pyroclastic falls, and ballistic. This image database is balanced by the five classes of roundness proposed by Rusell, Taylor, and Pettijohn. In addition, a data augmentation technique was used for better learning. The quality and realism of the generated images were evaluated using KDE and MMD metrics. The results indicated that WGAN generates good-quality images while maintaining class diversity.

ABSTRACT. The present paper proposes a study which aims the modeling and prediction of the urban expansion of the Zacatecas-Guadalupe cities (Mexico), using techniques such as Support Vector Machines (SVM), Random forests (RF), and the catBoost algorithm. According to the implementation of the previous techniques, the land use and the land cover maps corresponding to the period 2000–2020 were used, as well as the inclusion of socioeconomic, topographic and cultural attribute variables. The obtained results reveals that the proposed models make reasonable predictions of the urban expansion in the year 2030. Among the used techniques, the best algorithm for this problem was Soft SVM.

ABSTRACT. End-user performance demands continue to increase, the demand for throughput, ubiquitous coverage, as well as limitation on frequency spectrum, straining the Radio Access Network, (RAN), because of these, to meet these demands 5G technology makes use of the millimeter wave band (mm Wave), Massive MIMO, beam forming and highly integrated advanced antenna systems, therefore, in the side of the user device, the wireless mobile device must have with an array antenna, an essential component, enough small and favorable gain, that operates in the millimeter wave band, 24 -28 GHz . This work describes the design and simulation of a sub- array antennas as a part of an advance antenna system to operate in the mm Wave band.

ABSTRACT. This article presents a comparison between two-level and three-level bidirectional buck/boost converters, which are consolidated topologies and carry several relevant characteristics in automotive applications and thus brought to the choice of non-isolated converter, leading to minor electromagnetic interference, high efficiency, simplicity, lower size and less weight. This addresses the comparative analysis for a brief future implementation in a full-scale electric go-kart operating with an Axial-Flux Permanent Magnet Synchronous Machine with a 60kW peak power and 36kW continuous power. The main differences between the listed converters will be covered, highlighting a comparison in their efficiency characteristics and its implications. Furthermore, this analysis impacts ideal and real static gain, operational complexity and their association with supercapacitors to better take advantage of regenerative braking, as well as to provide an optimized converter design.

ABSTRACT. This paper presents the design of soft sensors based on supervised learning for a distillation column. The sensors are designed to estimate specific temperature values during the process. Three virtual sensors, based on three different models to determine the best performance, are developed considering a dataset obtained from the real process. The dataset contains 100 files, each corresponding to a different distillation process and containing from 6,000 to 10,000 data, the variables considered were the temperatures in every plate. The results demonstrate an estimation error between 0.28 to 0.41

ABSTRACT. Three-phase inverters are essential for applications like reactive power compensation in electric grids. However, their sensitivity to dynamic uncertainties requires closed-loop control schemes for effective management. Accurate dynamical models are crucial for designing these control schemes. This research proposes and validates two generalized analytical models using the Simscape library. These models can derive simplified control models or analyze inverter operation under various external factors and configurations. Results indicate that the proposed models are more flexible and computationally efficient than existing ones.

ABSTRACT. This paper presents a robust maximum power point tracking (MPPT) technique for photovoltaic (PV) panels. The proposed MPPT system aims to optimize power extraction from PV panels under varying environmental conditions, such as fluctuations in sunlight and temperature. The control strategy consists of a classic state feedback controller tuned via the $\mathcal{H}_\infty$ framework for disturbance attenuation. A DC-DC boost converter is employed for impedance matching, while the incremental conductance algorithm is utilized for real-time maximum power point (MPP) searching. Simulation results are provided, validating the effectiveness of the proposed approach in improving PV system performance.

ABSTRACT. Given the increasing number of wind power plants worldwide, it has become necessary to consider the effects and impacts of their high penetration on power grids. Due to their random nature, wind speeds require a stochastic modeling approach. This paper describes the main ideas of modeling based on stochastic differential equations to reproduce wind speed simulations. Furthermore, it extends the implementation of this approach to allow for smaller integration steps, demonstrating that the method can reproduce the statistical properties of wind speeds on these time scales of seconds.

ABSTRACT. With the increase of variable renewable energy (VRE) sources in the electric generation mix, there is a rising interest to investigate their impact on the power system operation and control. The main impacts of VRE are due to the power generation variability, the low short circuit current contribution, and their limited capability for frequency support. In this paper, the impact on frequency control and the capability to deliver frequency support by VRE that use voltage source inverters (VSI) is presented.

ABSTRACT. Solar greenhouses incorporate photovoltaic modules on the cover to transform solar irradiance into electricity. Modules with separate cells or conventional modules installed with separation can be used, predominating crystalline, monofacial or bifacial silicon technology. It is important to minimize the shadow of the agrivoltaic modules on the crops so as not to reduce their yield. Therefore, it is necessary to know the global transparency factor of the solar greenhouse cover to design and/or select agrivoltaic modules, contributing to sustainable agricultural practices and maximizing energy and agricultural performance. This work presents the design and construction of a graphical interface to estimate the optimal transparency level of a solar greenhouse cover according to the characteristics of the crop and the location, using a simplified agrivoltaic model. The verification results showed that the transparency values obtained with the interface present a deviation of ±4 % with respect to the reference model, and that the interface is portable, autonomous, efficient, intuitive and low-cost. Additionally, the transparency factor is calculated in real-time, with a response time of 8 seconds. This research allows maximizing solar energy production and limiting the loss of plant productivity to below 10 %, offering a valuable tool for farmers and solar greenhouse designers.

ABSTRACT. The deployment of microgrids (MGs) is a clear manifestation of the technological advances applied to electrical networks. In DC microgrids, several distributed energy resources (DERs) coexist for the single purpose of supplying the load locally. This is the case of cogenerators, photovoltaic and wind generators, which must smoothly operate in conjunction with battery energy storage systems (BESS). Unlike renewable plants that are placed where the primary source is available, BESS can be sited where the MG benefits most. One criterion for its siting can be the minimization of distribution power losses. This article addresses this problem by using an incremental power loss model which permits to find the optimal BESS siting in MGs. This is a practical approach formulated in terms of the MG topology, distribution line resistances, and nodal voltages, with which the best candidate node for BESS siting can be effectively found. The method is validated using a 13-node MG with three DERs.

ABSTRACT. In microgrids that operate in off-grid mode, with grid formers based on power electronic converters, one of the serious problems to be solved is performing black starts, mainly when the microgrid distribution network contains power transformers. This problem is fundamentally due to the inrush current associated with these devices. This article investigates the transformer energization process, considering a microgrid called CampusGrid, built on a university campus, as a case study. This study aims to identify the effects caused by the inrush current transformers on its operation. Simulation results from CampusGrid power systems performed in ATPDraw are presented.

ABSTRACT. This paper proposes a hybrid balancing method for energy storage in batteries using a buck-boost DC-DC converter. The method aims to achieve efficient balancing of the cells to enhance battery performance and longevity. Control of the converter is implemented using peak current control (PCC). We analyze both ideal and experimentally obtained waveforms of two series-connected cells to demonstrate the effectiveness of our proposed method. The experimental results validate the waveforms of the hybrid balancing approach, showing promising potential for application in Lithium polymer batteries.

ABSTRACT. Recent research on converter topologies and modulation strategies has advanced considerably. Cascaded H-bridge multilevel inverters are an alternative for medium and high-power systems; however, several topologies depend on having DC voltage sources of different levels to synthesize the multilevel AC waveform. This article describes the control of a 15-level non-modular cascaded H-bridge multilevel inverter. The topology includes six IGBTs and six complementary switching devices, a single DC voltage source, and two electrolytic capacitors. The power switch activation control technique is carried out through a hysteresis control based on monitoring the reference signal and balancing the voltages of the asymmetric capacitors. Using the output voltage and the voltages of two capacitors, the voltage levels are generated, according to a combinational logic that establishes which operating mode is the most appropriate to comply with the regulation of the reference signal and the voltage balance of the capacitors.

ABSTRACT. This paper describes the numerical analysis performed to estimate the losses of a 180 A,150 kHz common planar inductor used in a 350 V, 32 kW interleaved Buck-Boost converter. The design principle and operation of the planar inductor are briefly discussed firstly providing the physical characteristics of the constructed inductor. Later, a 3D COMSOL model of the common inductor is presented taking into account the core and winding material as well as its magnetic and thermal characteristics. Numerical results of the common inductor thermal behavior are finally presented assuming the electrical environment of the power converter circuit, which are analyzed and discussed at the end of the article mentioning proposals for heat dissipation.

ABSTRACT. This work presents a methodology to evaluate the utilization degree of medium voltage primary feeders when distributed generation sources are connected to meet a gradual annual load growth without causing overloads. As the load increases, the number of distributed generation sources also increases. The present methodology complements previous studies where effects such as voltage regulation, power factor, harmonics, losses in feeders, reverse power flows, etc., were analyzed to determine the maximum distributed generation capacity that can be connected without causing operational problems. This maximum capacity is referred to as Host Capacity. To consider the random states of operation, the Monte Carlo technique was used in a Python and OpenDSS environment. The methodology was validated using the IEEE 123-node network.

ABSTRACT. This paper presents a framework for the optimal placement of Electric Vehicle Charging Stations (EVCS) in unbalanced distribution grids, aiming to minimize power losses and voltage deviations throughout the day. By leveraging the synergy between Photovoltaic (PV) systems and EVCS, the proposed approach employs a Genetic Algorithm (GA) to address grid complexities. The optimal placement is determined by con- sidering dynamic changes in the daily profiles of PV, residential loads, and EVCS. When applied to an IEEE 34-node system with high PV penetration, this methodology leads to a 58% installation rate of EVCS, resulting in only 3.51% increase in line losses despite the significant additional EVCS load. Furthermore, this approach reduces the average voltage deviation index for the day by 1.55%. This installation procedure supports grid planning for distribution systems and offers practical insights into efficient EVCS deployment, underscoring the importance of grid balance and renewable energy integration

ABSTRACT. Industrial electrification provides sustainable possibilities to lower carbon footprints and improve energy efficiency within industrial complexes, particularly in petrochemical and heavy sectors. The electrification has also a significant impact on distribution and transmission networks. Due to a double radial distribution (DRD) design of power system architecture in industrial facilities, dual source supply is crucial. In order to accomplish this type of configuration, full backup power is usually provided through internal generation facilities, often in cogeneration assets. As a result, Transmission System Operators (TSOs) must strategically plan robust and reliable power networks, not only to meet the substantial power demand of large industrial complexes but also to handle impressive short-circuit power levels. This paper covers applications across various industries, including petrochemicals, gas treatment plants, oil and gas facilities, heavy industry, hydrogen generation processes, and ammonia plants. It highlights recent research developments that combine internal generation with high power demand, aiming at an improved reliability for the industrial users (measured by higher reliability standards). The paper explores load forecasting, system capacity, and alternative power system architectures to handle such substantial power requirements and short-circuit levels.

ABSTRACT. Power system resilience refers to the ability of an electrical power system to withstand and quickly recover from disruptive events such as natural disasters, equipment failures, cyberattacks, or other emergencies while maintaining its critical functions. Many proposals consider resilience metrics to evaluate the performance of a power system under these extreme events. Still, most proposals consider the resilience of the high-voltage bulk power grid and microgrids. Still, there are few medium- voltage and power distribution systems. Therefore, it does not assess the resilience of specific elements considered essential in a distribution system as electric protection. This paper develops a time-based quantitative metric to evaluate power system resilience when a compensated distance- based relay is used. Simulation on a modified IEEE34-bus system is employed to validate the effectiveness of compensated distance-based relay. The results show how relays’ response time can be improved over conventional protections, which enhances system resilience

ABSTRACT. This paper calculates and assigns the wheeling charge in deregulated power systems, considering the network usage and an allocated cost for each power transmission line by the Locational Marginal Price, so optimal power flows in alternating current are used. Using the 300-bus power flow test case results are obtained that reflect the impact of network usage while performing power transactions, compared to the traditional postage stamp method that is currently used in various electrical power systems around the world.

ABSTRACT. This paper presents a new approach for the analysis of angular and voltage stability of power systems, considering the uncertainty of the load magnitude, which is represented by the Ornstein-Uhlenbeck stochastic process. The uncertainty is considered in both, the initial stages of operation (power flow) and at each integration step of the time-domain simulation. The proposed approach performs the stability assessment of the N-1 contingencies of the IEEE 9-bus 3-machine test power system, and shows that considering the uncertainty of the load in the stability assessment, could change its results. It additionally allowed an initial test of the robustness of the ANFIS automatic voltage regulator.

ABSTRACT. Building Information Modeling (BIM) has primarily been applied to general construction projects, with limited attention to the unique requirements of electrical substations. This research addresses this gap by developing a BIM guide and catalog specifically for electrical substations, tailored to Latin America (Latam). This guide emphasizes the importance of defining the Level of Information Need (LOIN) and integrates Lean Construction principles to prevent model overload and ensure efficient lifecycle management. Additionally, we explore the future of substation management through Digital Twins and Virtual Reality, maintaining the single source of truth principle and enhancing various BIM uses such as Asset Management and Preventive Maintenance Scheduling. This paper presents an overview of our guide, shares experiences from Latam and Europe, and provides recommendations for effective BIM implementation in substation projects across the region.

ABSTRACT. Sensitivity assessment methods have been used in power systems for the rapid contingency analysis of system reliability in terms of power transmission and durability. This is due to concerns regarding power flow fluctuations in the grid caused by fluctuations in the output of renewable energy. The arbitrary polynomial chaos method (APC) has been proposed as a method for analyzing a probabilistic power flow. It treats the variation in renewable energy output in large-scale integrations as a probability distribution, and it can be performed with high accuracy and high speed. In an extended study of the APC, we propose a reliability analysis method for an assumed transmission line contingency by integrating the APC with a line outage distribution factor (LODF) in a sensitivity index for transmission line outages. First, in addition to the conventional N-2LODF, which represents a double-circuit simultaneous contingency, we derive the N-1LODF, which represents a single-circuit contingency. This derivation allows for N-2/N-1 contingencies to be analyzed simultaneously. Second, to verify the superiority of the proposed method, we conduct a reliability analysis and evaluation of the effect of wind power on an actual power transmission grid. In the analysis, capacity overload according to the probability distribution of transmission lines is considered. Finally, the wind power impact on the grid revealed by probabilistic power flow calculations focusing on distribution shapes will be discussed. This research can contribute to the development of a flexible power transmission grid for next-generation renewable energy output fluctuations.

ABSTRACT. Accurate estimation and characterization of the load demand curve are critical tasks for operators during the planning stage to ensure a secure and efficient power system operation. The aim is to coordinate the energy supply and the electricity market by predicting demand behavior, which must align with the scheduled generation levels at power plants. This paper evaluates the performance of machine learning methods from different categories such as linear regression (LR), multi-layer perceptron (MLP), gated recurrent unit (GRU), random forest (RF), and support vector regression (SVR) for short-term load demand forecasting from one-day to one-week-ahead. The objective is to identify the most effective methods for capturing the complex and dynamic nature of load demand, influenced by diverse factors and patterns. The dataset used includes the measured historical consumption data from Brazil, spanning 2016 to 2023, provided by the national electricity operator (ONS), and considers the four regions of the country. To assess the accuracy of the forecasts, the mean absolute percentage error (MAPE) and root mean squared error (RMSE) are calculated. Results show that MLP and GRU are comparatively superior to other ML methods, demonstrating consistent performance across all regions and providing more reliable estimates in terms of errors.

ABSTRACT. In this work, we propose a renewable energy generation system with a behavior similar to that of a conventional synchronous generator, in terms of inertial response and reactive power injection. The contributions of this work are: 1) the dynamic modeling of an inverter that includes its physical inertia and the corresponding inertial response to power imbalances between demand and generation, and 2) the synthesis of an optimal non-linear controller for the inverter system, where the injection of reactive power into the electrical grid is regulated. Simulation results are presented using a photovoltaic generator, to demonstrate the effectiveness of the proposed methodologies.

ABSTRACT. Power systems worldwide face profound technical challenges that require thorough analysis and evaluation to ensure proper system operation. One of the main aspects involved in these challenges focuses on the use of power electronics-based devices, specifically those using voltage source converters (VSC), which allow flexible operation of the power grids. In this work, a transient stability study for a practical power system with the integration of battery energy storage systems (BESS), static synchronous compensators (STATCOM), and solar photovoltaic (PV) is developed. The crux of this study is the use of a positive-sequence VSC model to represent the power devices, which seamlessly combine with the power system model. This permits to perform time-domain simulations in an efficient way with new topologies and size of power systems. The critical fault clearing time is determined to verify the impact of these technologies on power system transient response. The results demonstrate the impact of replacing synchronous generators (SG) with PV systems and the influence of BESS and STATCOM devices on the power grid with high PV penetration. The presented methodology is applied in the equivalent model of the Nueva Inglaterra power grid.

ABSTRACT. The increase in the penetration of variable renewable energy sources (VRES) has bring up new phenomena on power grids worldwide. In 2038 the Mexican electrical system will have 13,071 MW PV generation in the interconnected electrical system (SIN) and 725 MW in the Baja California system (BCA). With this photovoltaic generation the phenomenon called "duck curve" commonly occurs. This phenomenon is defined as the total consumption minus the generation of the VRES (photovoltaic (PV) generation) of the entire network on a given day and can bring about a temporary imbalance between the peak demand of the system due the decrement in photovoltaic generation during the sunset. A disturbance in this period can cause an imbalance in the frequency and provoke load shedding by frequency or the tripping of other generation units. This paper proposes the installation of an energy storage source (EES) through virtual inertia control to reduce the effects of the duck curve in the event of a disturbance.