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This article introduces a novel approach for developing an electrical model of the lithium batteries used in an electric ultralight aircraft. Currently, no method exists in the technical literature for accurately modeling the electrical characteristics of batteries in an electric aircraft, making this study a valuable contribution to the field. The proposed method was validated with an all-electric ultralight aircraft designed and constructed at the Pascual Bravo University Institution. To build the detailed model, a kinematic analysis was first conducted through takeoff tests, where data on the speed, acceleration, time, and distance required for takeoff were collected, along with measurements of the current and power consumed by the batteries. The maximum speed and acceleration of the aircraft were also recorded. These kinematic results were obtained using two batteries made from Samsung INR-18650-35E lithium-ion cells, and different wing configurations of the aircraft were analyzed to assess their impacts on the battery energy consumption. Additionally, the discharge cycles of the batteries were evaluated. In the second phase, laboratory tests were performed on the individual battery cells, and the Peukert coefficient was estimated based on the experimental data. Finally, using the Peukert coefficient and the kinematic results from the takeoff tests, the electrical model of the battery was fine tuned. This model allows for the creation of charging and discharging equations for ultralight lithium batteries. With the final electrical model and energy consumption data during takeoff, it becomes possible to determine the energy usage and flight range of an electric aircraft. The model indicated that the aircraft did not require a long distance to takeoff, as it reached the necessary takeoff speed in a very short time. The equations used to simulate the discharge cycles of the batteries and lithium cells accurately described their energy capacities.

This paper presents a novel grid-to-vehicle modeling framework that leverages probabilistic methods and neural networks to accurately forecast electric vehicle (EV) charging demand and overall energy consumption. The proposed methodology, tailored to the specific context of Medellin, Colombia, provides valuable insights for optimizing charging infrastructure and grid operations. Based on collected local data, mathematical models are developed and coded to accurately reflect the characteristics of EV charging. Through a rigorous analysis of criteria, indices, and mathematical relationships, the most suitable model for the city is selected. By combining probabilistic modeling with neural networks, this study offers a comprehensive approach to predicting future energy demand as EV penetration increases. The EV charging model effectively captures the charging behavior of various EV types, while the neural network accurately forecasts energy demand. The findings can inform decision-making regarding charging infrastructure planning, investment strategies, and policy development to support the sustainable integration of electric vehicles into the power grid.

Context: Time is a crucial issue in the simulation of power electronics (PE) devices, even more when these elements are integrated into microgrids. Method: This paper deals with the deduction of the average switch model for PE devices with the purpose of reducing simulation times. For doing this, the average model is only applied over the power switches of PE devices, not being applied over the complete topology as traditionally done. The proposed average model switch permits eliminating the ripple of voltage and currents but keeping the transient of the signals. The average model switch is derived for Boost and Buck converter switches and then generalized to power inverter switches. The proposed approach is validated using OpenModelica software. Results: A system featuring a battery, a DC/DC converter, and an inverter connected to the power grid was simulated. A comparison was performed between a simulation that considers the power switches and a simulation that uses the proposed average model switch, the time simulation was reduced up to 99.788 %, which validates the proposed approach. Conclusions: The proposed average switch model significantly reduces simulation times. This method offers a promising way to streamline power electronics device simulations, particularly in the context of microgrids and other applications where time efficiency is critical.

Context: In power electronics applications, it is important to make comparisons between converters to choose the device that best suits a particular application. This paper compares the Boost converter and the hybrid Boost converter. The operating models of both converters under study are developed and explained in detail to allow for a proper comparison and analysis. Method: Using the passive sign law, the differential equations that govern the behavior of each converter are determined upon the basis of their switching states. Then, circuit simulations are performed by using the OpenModelica software to analyze the output signals of both converters with the same input parameters. Results: Comparisons of voltage and current gains, current and voltage time response, and ripple were obtained. Additionally, the efficiency was analyzed by adding resistive losses in each passive element of both converters. Conclusions: For high duties, the hybrid Boost converter has a greater capacity to increase the output voltage than the Boost converter. It was also found that the hybrid Boost converter has a low overshoot and a low ripple in the time response of its output signals. However, this converter is less efficient.

This paper presents a methodology for obtaining the voltage and current references that permits the modelling of Hybrid Cuk (HC) Converters. The passive sign convention when system elements absorb energy is used to derive the differential equations governing the behaviour of the converter. This allows the explanation of the operation principle of the converter that is performed through the switching states. The behavior of the converter is studied by means of an energy interchange analysis between inductors and capacitors. After the deduction of the references and operating principle, the differential equations (DEs) that allow the modelling of the converter are deduced. Finally, a comparison between the mathematical model and the circuital representation of the converter is performed in OpenModelica for validation purposes. The simulations’ results allow to conclude that the obtained references and differential equations are coherent with the circuital implementation.

The data presented in this paper are related to the paper entitled “A Numerical Method for Solving Caputo's and Riemann-Liouville's Fractional Differential Equations which includes multi-order fractional derivatives and variable coefficients”, available in the “Communications in Nonlinear Science and Numerical Simulation” journal. Here, data are included for three of the four examples of Fractional Differential Equation (FDE) reported in [1], the other data is already available in [1]. Data for each example contain: the interval of the solution, the solution by using the proposed method, the analytic solution and the absolute error. Data were obtained through Octave 5.1.0 simulations. For a better comprehension of the data, a pseudo-code of three stages and nine steps is included.

Protecting AC microgrids (MGs) is a challenging task due to their dual operating modes—grid-connected and islanded—which cause sudden variations in fault currents. Traditional protection methods may no longer ensure network security. This paper presents a novel approach to protection coordination in AC MGs using non-standard features of directional over-current relays (DOCRs). Three key optimization variables are considered: Time Multiplier Setting (TMS), the plug setting multiplier’s (PSM) maximum limit, and the standard characteristic curve (SCC). The proposed model is formulated as a mixed-integer nonlinear programming problem and solved using four metaheuristic techniques: the genetic algorithm (GA), Imperialist Competitive Algorithm (ICA), Harmonic Search (HS), and Firefly Algorithm (FA). Tests on a benchmark IEC MG with distributed generation and various operating modes demonstrate that this approach reduces coordination times compared to existing methods. This paper’s main contributions are threefold: (1) introducing a methodology for assessing the optimal performance of different standard curves in MG protection; (2) utilizing non-standard characteristics for optimal coordination of DOCRs; and (3) enabling the selection of curves from both North American and European standards. This approach improves trip time performance across multiple operating modes and topologies, enhancing the reliability and efficiency of MG protection systems.

Passive Optical Networks (PONs) are telecommunication technologies that use fiber-optic cables to deliver high-speed internet and other communication services to end users. PONs split optical signals from a single fiber into multiple fibers, serving multiple homes or businesses without requiring active electronic components. PONs planning involves designing and optimizing the infrastructure for delivering fiber-optic communications to end users. The main contribution of this paper is the introduction of tailored operators within a genetic algorithm (GA) optimization approach for PONs planning. A three vector and an aggregator vector are devised to account, respectively, for physical and logical connections of the network, facilitating the execution of GA operators. This codification and these operators are versatile and can be applied to any population-based algorithm, not limited to GAs alone. Furthermore, the proposed operators are specifically designed to exploit the unique characteristics of PONs, thereby minimizing the occurrence of unfeasible solutions and accelerating convergence towards an optimal network design. By incorporating these specialized operators, this research aims to enhance the efficiency of PONs planning, ultimately leading to reduced costs and improved network performance.

The data presented in this paper are related to the paper entitled “Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection”, available in the Energies journal. Here, data are included to show the results of an Under-Frequency Load Shedding (UFLS) scheme that considers the injection of virtual inertia by a VSC-HVDC link. The data obtained in six cases which were considered and analyzed are shown. In this paper, each case represents a different frequency response configuration in the event of generation loss, taking into account the presence or absence of a VSC-HVDC link, traditional and optimized UFLS schemes, as well as the injection of virtual inertia by the VSC-HVDC link. Data for each example contain the state of the relay, threshold, position in every delay, load shed, and relay configuration parameters. Data were obtained through Digsilent Power Factory and Python simulations. The purpose of this dataset is so that other researchers can reproduce the results reported in our paper.

This paper introduces an improved methodology designed to address a practical deficit of existing methodologies by incorporating circuit-level analysis in the assessment of building microgrid reliability. The scientific problem at hand involves devising a systematic approach that integrates circuit modeling, Probability Density Function (PDF) selection, formulation of reliability functions, and Fault Tree Analysis (FTA) tailored specifically for the distinctive features of building microgrids. This method entails analyzing inter-component relationships to gain comprehensive insights into system behavior. By harnessing the circuit models and theoretical framework proposed herein, precise estimations of microgrid failure rates can be attained. To complement this approach, we propose a thorough investigation utilizing reliability curves and importance measures, providing valuable insights into individual device failure probabilities over time. Such time-based analysis plays a crucial role in proactively identifying potential failures and facilitating efficient maintenance planning for microgrid devices. We demonstrate the application of this methodology to the University of Antioquia (UdeA) Microgrid, a low-voltage system comprising critical components such as solar panels, microinverters, inverters/chargers, batteries, and charge controllers.

Under-frequency load shedding (UFLS) schemes are the latest safety measures applied for safeguarding the integrity of the grid against abrupt frequency imbalances. The overall inertia of electrical power systems is expected to decrease with an increased penetration of renewable energy as well as elements connected through power electronic interfaces. However, voltage source converter-based high voltage direct current (VSC-HVDC) links can provide virtual inertia through a control loop that allows for a reaction to occur at certain frequency fluctuations. This paper evaluates a UFLS scheme that considers the injection of virtual inertia through a VSC-HVDC link. A genetic algorithm (GA) is used to determine the location of the UFLS relays, the activation threshold of each stage, the delay time and the percentage of load shedding at each stage. It was found that the virtual inertia causes the nadir to delay and sometimes reach a greater depth. Furthermore, the implemented GA approximates the frequency response to the limits set with the constraints, reducing the load shedding but achieving a steeper nadir and a lower steady-state frequency level than traditional UFLS. The simulations were performed using the IEEE 39-bus test system.

This paper addresses the protection coordination problem of microgrids combining unsupervised learning techniques, metaheuristic optimization and non-standard characteristics of directional over-current relays (DOCRs). Microgrids may operate under different topologies or operative scenarios. In this case, clustering techniques such as K-means, balanced iterative reducing and clustering using hierarchies (BIRCH), Gaussian mixture, and hierarchical clustering were implemented to classify the operational scenarios of the microgrid. Such scenarios were previously defined according to the type of generation in operation and the topology of the network. Then, four metaheuristic techniques, namely, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Invasive Weed Optimization (IWO), and Artificial Bee Colony (ABC) were used to solve the coordination problem of every cluster of operative scenarios. Furthermore, non-standard characteristics of DOCRs were also used. The number of clusters was limited to the maximum number of setting setting groups within commercial DOCRs. In the optimization model, each relay is evaluated based on three optimization variables, namely: time multiplier setting (TMS), the upper limit of the plug setting multiplier (PSM), and the standard characteristic curve (SCC). The effectiveness of the proposed approach is demonstrated through various tests conducted on a benchmark test microgrid.

The increasing presence of electric vehicles (EVs) requires a thorough understanding of their impact on distribution assets. EV chargers are characterized as nonlinear and multi-state loads due to their unique electrical consumption patterns. This paper presents a comprehensive study focused on modelling diverse EV charging units deployed in the industrial, commercial, and residential sectors. To conduct this analysis, a simulation environment utilizing the ETAP tool is employed, taking into account time variations. This approach facilitates the comprehension and anticipation of the effects that EV charging may impose on distribution networks, thereby supporting well-informed decision-making for the adaptation and enhancement of the electrical system. Detailed data on current and voltage from operational EV charging stations were collected to create consumption, current, voltage, and harmonic profiles for these charging units. Subsequently, general models or libraries applicable to Level 1, Level 2 and Level 3 EV chargers available in Colombia were developed. These models underwent rigorous validation and were subjected to a comprehensive analysis using the IEEE 13-bus test system. The research yielded valuable insights and conclusions regarding the integration of EV chargers into the current Colombian distribution systems, as well as the potential impact of adopting these devices on power quality issues within the distribution grid. This study contributes to the improved management of distribution assets, thereby facilitating the integration of sustainable electric mobility in the national electrical system.

Main objective of this paper is the optimal distribution of the fundamental non-efficient load current terms between the inverters —Energy Gateways (EGs)— connected in grid-tied microgrids (MGs). The main feature of the presented approach is the use of the EGs as controlled current sources that can compensate fundamental non-efficient load current terms in addition to the generation of fundamental positive-sequence active current, avoiding the use of shunt active power filters. The approach relies on the so-called System of Constant References (SoCR) that is based on the symmetrical components decomposition and dq0 transformation. SoCR procedure decouples efficient and non-efficient components of the instantaneous load currents, transforming all of them into six constant references. The optimization algorithm uses a new approach for the calculation of the peak currents in each phase, avoiding non-convex problems when determining the currents of the EGs considering their operating limits. A medium-power size MG that includes photovoltaic and wind generators, as well as, a battery energy storage system is considered to evaluate the capabilities of the proposal. There were evaluated four scenarios: baseline, balanced distribution, proportional distribution, and optimal distribution. All scenarios, except optimal distribution scenario, surpass the current limits for the EGs connected. The results highlight the benefits of using the EGs as active agents in MG efficient operation and demonstrate how the optimization approach achieves the goal of maintaining the generation capabilities of EGs at the same time that compensates the non-efficient current terms demanded by the load.

This paper proposes a Python tool for the analysis of balanced, unbalanced, sinusoidal and nonsinusoidal three-phase systems based on the IEEE Std. 1459. The proposed tool was developed to be used by electrical engineers and professionals of related careers providing a free access for electrical power calculations that allows validation and power analysis in power systems. The proposed tool is available for use on a GitHub link and only requires voltage and current signals as inputs, making it possible to dispense expensive commercial network analyzers. The tool is made of two interfaces, one for making power calculations of sinusoidal three-phase systems and the other one for making calculations of nonsinusoidal three-phase systems; both interfaces can be accessed through a main menu. The proposed tool facilitates decision-making in electrical system issues, which helps improving the efficiency and power quality of electrical systems.

Electric vehicles (EVs) have gained considerable attention in the last decade due to a paradigm shift in the transport sector driven by a higher awareness of environmental issues. While the importance of EVs cannot be overstated in the context of the global climate crisis, it does raise the question of whether certain countries or states are ready for their implementation. It is, therefore, necessary to analyze the impact of EVs in the power grids of these countries and states, considering factors such as line congestion and the eventual degradation of voltage profiles, to determine their hosting capacity and assess eventual expansion options. This paper proposes a representative prototype of Curaçao’s electrical system, which is used for assessing the impacts of EVs, allowing us to determine its hosting capacity. Curaçao is an island in the southern Caribbean Sea that uses fuel generators, wind energy, and solar energy to generate electricity. The idea behind this paper is to analyze the effects caused by an increase in EVs on Curaçao’s power grid and propose preventive measures to deal with such problems. Eight EV charging stations were considered, one DC super fast-charging station, three normal DC fast-charging stations, and four AC fast-charging stations. In 2022, there were an estimated 82,360 vehicles on the island. Using this information, this paper analyzes how many vehicles can be simultaneously connected to the grid before it no longer operates under acceptable values. The results showed that 3.5% of the total vehicles can be hosted by the grid. Nonetheless, this can be increased up to 4.5% with the reinforcement of a transmission line.

Sliding-mode control (SMC) is a robust technique used in power electronics (PE) for controlling the behavior of power converters. This paper presents simulations and experimental results of an optimal SMC strategy applied to Semi-Bridgeless Boost Converters (SBBC), which includes Power Factor Correction (PFC). As the main contribution, the optimal coefficients of the SMC strategy are obtained using two metaheuristic approaches, namely the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The main objective is to obtain the sliding coefficients that ensure the best converter response in terms of the input current and output voltage, both during start-up and under disturbances (including changes in load, source, and references). The fitness function to be minimized includes two coefficients, namely the Integrative Absolute Error (IAE) and the Integral Time Absolute Error (ITAE), for both the input current and output voltage. These coefficients measure the converter’s effort to follow the control references. The IAE penalizes errors during start-up, whereas the ITAE penalizes errors in the steady state. The tests carried out demonstrated the effectiveness of the GA and PSO techniques in the optimization process; nonetheless, the GA outperformed the PSO approach, providing sliding coefficients that allowed for a reduction in the input current overshoot during start-up of up to 24.15% and a reduction in the setting time of the output voltage of up to 99%. The experimental results were very similar when tuning with the GA and PSO techniques; nevertheless, tuning with the GA technique produced a better response in the face of disturbances compared to the PSO technique.

Nowadays, microgrids play a significant role in the development of modern distribution networks (DNs). In this context, the ever increasing presence of renewable distributed generation (DG) in microgrids has imposed new challenges in protection coordination, rendering traditional coordination schemes inefficient. Thus, it is essential to explore new approaches for protection coordination that take into account specific features of microgrids, such as the presence of DG and the fact that they may operate under different operational modes or topologies. This paper proposes a novel approach for the optimal coordination of over-current relays (OCRs) in microgrids that considers three optimization parameters for each relay: time multiplying setting, plug setting current and characteristic curve. To show the applicability of the proposed approach, several tests were carried out using an IEC benchmark microgrid and a modified version of the IEEE 30 bus test system. Three metaheuristic techniques were implemented for solving the optimal coordination problem, namely: a genetic algorithm (GA), particle swarm optimization (PSO) and a teaching–learning based optimization (TLBO) algorithm. A comparison of results with other coordination models reported in the specialized literature evidenced the applicability and effectiveness of the proposed approach.

Protection coordination of AC microgrids (MGs) is a challenging task since they can operate either in grid-connected or islanded mode which drastically modifies the fault currents. In this context, traditional approaches to protection coordination, that only consider the time multiplier setting (TMS) as a decision variable may no longer be able to guarantee network security. This paper presents a novel approach for protection coordination in AC MGs that incorporates non-standard characteristic features of directional over-current relays (OCRs). Three optimization variables are considered for each relay: TMS, maximum limit of the plug setting multiplier (PSM) and standard characteristic curve (SCC). The proposed model corresponds to a mixed integer non-linear programming problem. Four metaheuristic techniques were implemented for solving the optimal coordination problem, namely: particle swarm optimization (PSO), genetic algorithm (GA), teaching-learning based optimization (TLBO) algorithm and shuffled frog leaping algorithm (SFLA). Numerous tests were run on an IEC MG as well as with the distribution portion of the IEEE 30-bus test system. Both systems incorporate distributed generation (DG) and feature several modes of operation. A comparison was made with other MG protection coordination approaches proposed in the specialized literature. In all cases, the proposed approach found reduced coordination times, evidencing the applicability and efficacy of the proposed approach.

This paper deals with the optimal sizing of islanded microgrids (MGs), which use diesel generators to supply energy in off-grid areas. The MG under study integrates photovoltaic (PV) and diesel generation, a battery energy storage System (BESS), and an inverter for the connection between AC and DC voltage buses. Levelised cost of energy (LCOE) and annual system cost (ASC) are considered economic indicators, while the loss of power supply probability (LPSP) is used as a reliability indicator. Fiscal incentives such as the tax benefits and accelerated depreciation applied in Colombia are considered for the optimally sizing of each MG element. Solar measurements were taken at a weather station located in the main campus of Universidad de Antioquia in Medellin, Colombia at a latitude of 6.10 and longitude of −75.38. The objective function is the minimization of the total energy delivered from the power sources that successfully meets the load. The model was implemented in Python programming language considering several scenarios. Two cases were evaluated: the first one considered PV panels, a BESS and a diesel generator, while the second one only considered PV panels and a BESS. The option that does not include the diesel generator turned out to be the most expensive, since additional PV and BESS resources are required to meet the load profile. Furthermore, it was found that the LCOE was lower when tax benefits were taken into account.

This paper presents the implementation of an AC three-phase programmable electronic load (PEL) that emulates load profiles and can be used for testing equipment in microgrids (MGs). The implemented PEL topology is built with a voltage source inverter (VSI) which works as a current controlled source and a Buck converter which permits the dissipation of active power excess. The PEL operation modes according to the interchange of active and reactive power and its operation in four quadrants were determined. The power and current limits which establish the control limitations were also obtained. Three control loops were implemented to independently regulate active and reactive power and ensure energy balance in the system. The main contribution of this paper is the presentation a detailed analysis regarding hardware limitations and the operation of the VSI and Buck converter working together. The PEL was implemented for a power of 1.8 kVA. Several experimental results were carried out with inductive, capacitive, and resistive scenarios to validate the proper operation of the PEL. Experimental tests showed the correct behavior of the AC three-phase currents, VSI input voltage, and Buck converter output voltage of the PEL for profile changes, including transient response.

This paper addresses the problem of Distribution Systems Reconfiguration (DSR), which consists of finding the state of switching devices (open or closed) in a given distribution network, aiming to minimize active power loses. DSR is modeled as a mixed-integer non-linear optimization problem, in which the integer variables represent the state of the switches, and the continuous variables represent the power flowing through the branches. Given the multi-modal and non-convex nature of the problem, an improved harmony search (IHS) algorithm is proposed to solve the DSR problem. The main novelty of this approach is the inclusion of a Path Relinking phase which accelerates convergence of the DSR problem. Several tests were carried out in four benchmark distribution systems, evidencing the effectiveness and applicability of the proposed approach.

This paper deals with circuit breakers (CBs) used in direct current microgrids (DCMGs) for protection against electrical faults, focusing on their evolution and future challenges in low voltage (

In recent years, distributed generation (DG) has become more common in modern distribution networks (DNs). The presence of these small-scale generation units within a DN brings new challenges to protection engineers, since short-circuit currents tend to increase; additionally, as with microgrids, modern DNs may feature several operational modes depending on their topology and the availability of DG. This paper presents a methodology for the optimal coordination of overcurrent relays (OCRs) in modern DNs with a high presence of DG. Given the fact that protection coordination is a non-linear and non-convex optimization problem, a hybrid harmony search and simulated annealing (HS-SA) approach was implemented for its solution and compared against other techniques, such as conventional HS, genetic algorithm (GA), particle swarm optimization (PSO) and hybrid PSO-HS. Several tests were performed on a DN, considering different operative scenarios as a function of the DG available within the network. A comparison with other works reported in the specialized literature was carried out, evidencing the applicability and effectiveness of the HS-SA technique in solving the optimal OCR coordination problem in modern DNs.

Power system restoration must be accomplished as soon as possible after a blackout. In this process, available black-start (BS) units are used to provide cranking power to non-black-start (NBS) units so as to maximize the overall power system generation capacity. This procedure is known as the generation start-up problem, which is intrinsically combinatorial with complex non-linear constraints. This paper presents a new mixed integer linear programming (MILP) formulation for the generation start-up problem that integrates non-conventional renewable energy sources (NCRES) and battery energy storage systems (BESS). The main objective consists of determining an initial starting sequence for both BS and NBS units that would maximize the generation capacity of the system while meeting the non-served demand of the network. The nature of the proposed model leads to global optimal solutions, clearly outperforming heuristic and enumerative approaches, since the latter may take higher computational time while the former do not guarantee global optimal solutions. Several tests were carried out on the IEEE 39-bus test system considering BESS as well as wind and solar generation. The results showed the positive impact of NCRES in the restoration processes and evidenced the effectiveness and applicability of the proposed approach. It was found that including NCRES and BESS in the restoration process allows a reduction of 24.4% of the objective function compared to the classical restoration without these technologies.

Power Factor Correction (PFC) single-phase AC/DC converters are used in several power electronics applications as full wave control rectifiers improving power quality and providing high standards of efficiency. Many papers dealing with the description or use of such topologies have been published in recent years; however, a review that describes and organizes their specific details has not been reported in the technical literature. Therefore, this paper presents an extensive review of PFC single-phase AC/DC converters operating with the Boost converter topology for low and medium voltage as well as and power appliances. A categorization of bridge, semi-bridgeless, and bridgeless, in accordance with the construction characteristics, was carried out in order to unify the technical terminology. Benefits and disadvantages are described and analyzed in detail. Furthermore, a comparison performance in terms of PFC, Total Harmonic Distortion (THD), power capacity, electromagnetic compatibility (EMC), number of elements, and efficiency is included.

Microgrids (MGs) are decentralized systems that integrate distributed energy resources and may operate in grid-connected or islanded modes. Furthermore, MGs may feature several topologies or operative scenarios. These characteristics bring about major challenges in determining a proper protection coordination scheme. A new optimal coordination approach for directional over-current relays (OCRs) in MGs is proposed. In this case, a clustering of operational models is carried out by means of a K-means algorithm hybridized with the principal component analysis (PCA) technique. The number of clusters is limited by the number of setting groups of commercially available relays. The results carried out on a benchmark IEC microgrid evidence the applicability and effectiveness of the proposed approach.

This paper proposes a new sliding surface for controlling a Semi-Bridgeless Boost Converter (SBBC) which simultaneously performs Power Factor Correction (PFC) and DC bus regulation. The proposed sliding surface is composed of three terms: First, a normalized DC voltage error term controls the DC bus and rejects DC voltage disturbances. In this case, the normalization was performed for increasing system robustness during start-up and large disturbances. Second, an AC current error term implements a PFC scheme and guarantees fast current stabilization during disturbances. Third, an integral of the AC current error term increases stability of the overall system. In addition, an Adaptive Hysteresis Band (AHB) is implemented for keeping the switching frequency constant and reducing the distortion in zero crossings. Previous papers usually include the first and/or the second terms of the proposed sliding surface, and none consider the AHB. To be best of the author’s knowledge, the proposed Sliding Mode Control (SMC) is the first control strategy for SBBCs that does not require a cascade PI or a hybrid PI-Sliding Mode Control (PI-SMC) for simultaneously controlling AC voltage and DC current, which gives the best dynamic behavior removing DC overvoltages and responding fast to DC voltage changes or DC load current perturbations. Several simulations were carried out to compare the performance of the proposed surface with a cascade PI control, a hybrid PI-SMC and the proposed SMC. Furthermore, a stability analysis of the proposed surface in start-up and under large perturbations was performed. Experimental results for PI-SMC and SMC implemented in a SBBC prototype are also presented.

This paper presents methodology for determining angular displacement (connections or vector group (colloquial)) in three-phase transformers. The proposed methodology is useful to determine the angular displacement if connections are known or the connections if angular displacement is known. Even though connection of three-phase transformers requires knowledge of angular displacement; the technical literature does not report a rigorous methodology or procedure for their explicit step by step determination. However, there are numerous applications in the areas of engineering and research that use time group connections. This paper presents the methodology for determining the Yy, Yd, Dy, Dd, Zy and Zd connections; including the use of time groups in some relevant engineering applications. The proposed methodology can be applied for commercial and noncommercial transformers, and also for power and distribution transformers.

Este artículo propone un modelo para rectificadores elevadores PFC (Power Factor Correction por sus siglas en inglés) sin puente para propósitos de control y basado en el análisis del promedio de pequeña señal. A partir de las leyes circuitales, cuatro modos de operación son definidos y explicados, asegurando una relación entre las variables físicas del convertidor. Basados en el modelo propuesto, dos lazos cerrados de control compuestos por controladores lineales Proporcionales e Integrales (PI) son propuestos. Algunas consideraciones de diseño para dimensionar los elementos reactivos son incluidas, de tal forma que se obtienen valores mínimos para su inductancia y capacitancia. Se presenta la implementación de un prototipo de 900 W con resultados experimentales que permite validar y reafirmar el modelo propuesto. Los resultados experimentales demuestran que el uso del convertidor PFC permite elevar el factor de potencia FP a 0,99 o más y reducir el THDi (Total Harmonic Distortion of the Current por sus siglas en Inglés) a 3,9 %, además de controlar el bus DC en la salida. Se verifica experimentalmente que el convertidor PFC desarrollado está de acuerdo con los estándares de calidad de la potencia EN 61000-3-2 (IEC 1000-3-2).

Microgrids constitute complex systems that integrate distributed generation (DG) and feature different operational modes. The optimal coordination of directional over-current relays (DOCRs) in microgrids is a challenging task, especially if topology changes are taken into account. This paper proposes an adaptive protection approach that takes advantage of multiple setting groups that are available in commercial DOCRs to account for network topology changes in microgrids. Because the number of possible topologies is greater than the available setting groups, unsupervised learning techniques are explored to classify network topologies into a number of clusters that is equal to the number of setting groups. Subsequently, optimal settings are calculated for every topology cluster. Every setting is saved in the DOCRs as a different setting group that would be activated when a corresponding topology takes place. Several tests are performed on a benchmark IEC (International Electrotechnical Commission) microgrid, evidencing the applicability of the proposed approach.

This paper presents the robust control of Three-Leg Split-Capacitor Shunt Active Power Filters (TLSC SAPFs) by means of structured H∞ controllers for reactive, unbalanced, and harmonic compensation and the DC-link bus voltage regulation. Robust controller synthesis is performed based on the TLSC SAPF dynamical model including power losses in passive elements. Before the control implementation, a systematic procedure for the nonlinear controllability verification of the converter and its quantification using the set-theoretic approach is presented. Controllability verification serves to accurately design the SAPF’s operation region. Thus, a Voltage Oriented Control (VOC) structure is implemented by using two different approaches to determine the PI controller parameters: (1) the traditional Pole-Placement method (PP-PI) and (2) the H∞-PI structured synthesis approach, which leads to PI robust controllers. From the latter approach, two sets of parameters are obtained. The first set considers the nominal model (H∞-PI), and the second one explicitly accounts for the model parametric uncertainties (H∞-uPI). An optimization procedure is presented for obtaining the optimal H∞-PI and H∞-uPI controller parameters where four complementary constrains are defined to establish a trade-off between the controllers performance and robustness. The enforcement of constraints is later evaluated for each of three PI controllers obtained. This work aims to establish a common ground for the comparison of robust control strategies applied to TLSC APFs; therefore, the TLSC SAPF compensation performance is measured and compared with the performance indices: integral of the absolute error (IAE), integral of the time-weighted absolute error (ITAE), integral of the absolute control action (IUA), and maximum sensitivity (Ms).

The optimal coordination of overcurrent relays (OCRs) has recently become a major challenge owing to the ever-increasing participation of distributed generation (DG) and the multi-looped structure of modern distribution networks (DNs). Furthermore, the changeable operational topologies of microgrids has increased the complexity and computational burden to obtain the optimal settings of OCRs. In this context, classical approaches to OCR coordination might no longer be sufficient to provide a reliable performance of microgrids both in the islanded and grid-connected operational modes. This paper proposes a novel approach for optimal coordination of directional OCRs in microgrids. This approach consists of considering the upper limit of the plug setting multiplier (PSM) as a variable instead of a fixed parameter as usually done in traditional approaches for OCRs coordination. A genetic algorithm (GA) was implemented to optimize the limits of the maximum PSM for the OCRs coordination. Several tests were performed with an IEC microgrid benchmark network considering several operational modes. Results showed the applicability and effectiveness of the proposed approach. A comparison with other studies reported in the specialized literature is provided showing the advantages of the proposed approach.

En este artículo se presenta un algoritmo genético especializad o aplicado a la solución del problema de interdicción. Este pro blema consiste en la interacción de un agente disruptivo que pretende maximiza r el daño al sistema de potencia (medido en deslastre de carga) , y el operador del sistema que impleme nta acciones correctivas para m inimizar el deslastre de carga. Este problema, también conocido como “el problema del terrorista,” es f ormulado en una estructura de programación binivel y solucionado mediante un algoritmo genét ico. La solución identifica los corredores más vulnerables de la red en términos de un ataque terrorist a, suministrando señales para futuros refuerzos de la red o vigilancia más estri cta de activos críticos. El enf oque propuesto ha sido probado en tres casos de estudio: un sis tema didáctico de 5 barras, un prototipo del sistema colombiano y el sistema d e pruebas de confiabilidad IEEE. Los resultados muestran la ro bustez y aplicabilidad de la metodología propuesta.

This paper proposes the application of a cradle-to-grave multi-pronged methodology to obtain a more realistic carbon footprint (CF) estimation of electro-intensive power electronic (EIPE) products. The literature review shows that methodologies for establishing CF have limitations in calculation or are not applied from the conception (cradle) to death (grave) of the product; therefore, this paper provides an extended methodology to overcome some limitations that can be applied in each stage during the life cycle assessment (LCA). The proposed methodology is applied in a cradle-to-grave scenario, being composed of two approaches of LCA: (1) an integrated hybrid approach based on an economic balance and (2) a standard approach based on ISO 14067 and PAS 2050 standards. The methodology is based on a multi-pronged assessment to combine conventional with hybrid techniques. The methodology was applied to a D-STATCOM prototype which contributes to the improvement of the efficiency. Results show that D-STATCOM considerably decreases CF and saves emissions taken place during the usage stage. A comparison was made between Sweden and China to establish the environmental impact of D-STATCOM in electrical networks, showing that saved emissions in the life cycle of D-STATCOM were 5.88 and 391.04 ton CO2eq in Sweden and China, respectively.

This paper proposes a strategy for sizing a battery energy storage system (BESS) that supports primary frequency regulation (PFR) service of solar photo-voltaic plants. The strategy is composed of an optimization model and a performance assessment algorithm. The optimization model includes not only investment costs, but also a novel penalty function depending on the state of charge (SoC). This function avoids the existence of a potential inappropriate SoC trajectory during BESS operation that could impede the supply of PFR service. The performance assessment algorithm, fed by the optimization model sizing results, allows the emulation of BESS operation and determines either the success or failure of a particular BESS design. The quality of a BESS design is measured through number of days in which BESS failed to satisfactorily provide PFR and its associated penalization cost. Battery lifetime, battery replacements, and SoC are also key performance indexes that finally permit making better decisions in the election of the best BESS size. The inclusion of multiple BESS operational restrictions under PFR is another important advantage of this strategy since it adds a realistic characterization of BESS to the analysis. The optimization model was coded using GAMS/CPLEX, and the performance assessment algorithm was implemented in MATLAB. Results were obtained using actual frequency data obtained from the Colombian power system; and the resulting BESS sizes show that the number of BESS penalties, caused by failure to provide PFR service, can be reduced to zero at minimum investment cost.

In this paper, a model and solution approach for minimizing internal power losses in Transformers Connected in Parallel (TCP) with tap-changers is proposed. The model is based on power chargeability balance and seeks to keep the load voltage within an admissible range. For achieving this, tap positions are adjusted in such a way that all TCP are set in similar/same power chargeability. The main contribution of this paper is the inclusion of several construction features (rated voltage, rated power, voltage ratio, short-circuit impedance and tap steps) in the minimization of power losses in TCP that are not included in previous works. A Genetic Algorithm (GA) is used for solving the proposed model that is a system of nonlinear equations with discrete decision variables. The GA scans different sets for tap positions with the aim of balancing the power supplied by each transformer to the load. For this purpose, a fitness function is used for minimizing two conditions: The first condition consists on the mismatching between power chargeability for each transformer and a desired chargeability; and the second condition is the mismatching between the nominal load voltage and the load voltage obtained by changing the tap positions. The proposed method is generalized for any given number of TCP and was implemented for three TCP, demonstrating that the power losses are minimized and the load voltage remains within an admissible range.

This paper proposes a deterministic algorithm to scale the reference currents of a shunt active power compensator (SAPC) based on IEEE Std. 1459 power decomposition when SAPC maximum output compensating current is going to be exceeded. The selective SAPC is proposed to improve power quality and energy efficiency in power networks by means of the cancelation or reduction of the non-efficient powers (Q1+, SU1, SeN). The non-efficient powers can be reduced in six possible sequences according to the priority of compensation. When SAPC maximum output current capacity is exceeded, the proposed algorithm limits the SAPC output compensating currents and the non-efficient currents can only be partially reduced in the power network. The reduction of the non-efficient powers depends on the selected compensation sequence. Experimental results for several compensation sequences demonstrate the appropriate operation of the selective SAPC using the proposed scaling algorithm.

ABSTRACT This paper presents a breakdown cost methodology to evaluate Levelized Costs of Electricity for large-scale Photovoltaic (PV) plants. The breakdown is based on a comprehensive taxonomy to evaluate Investment Costs (IC) and Operation and Maintenance (O&M) expenditures. We added an IC disaggregation level, called elements, on top of the five-component breakdown of the Energy Information Administration (EIA). In addition, a novel structure for disaggregating O&M costs is also proposed. The methodology is evaluated over a 20-MW and a 150-MW PV power plant hypothetically placed in the municipality of Uribia (Guajira Colombia). Also deterministic sensitivity analysis based on discount rate (WACC, Weighted Average Capital Cost), energy generated, O&M costs and IC is performed to aid investors in their decisions.