Continuous monitoring of electrical parameters is essential for understanding energy consumption, assessing power quality, and analyzing load behavior. This paper presents a dataset comprising measurements of three-phase voltages and currents, active and reactive power (per phase and total), power factor, and system frequency. The data was collected between April and December 2024 in the low-voltage system of a university laboratory, using high-accuracy power analyzers installed at the point of common coupling. Measurements were recorded every 10 min, generating 79 files with 432 records each, for a total of approximately 34,128 entries. To ensure data quality, the values were validated, erroneous entries removed, and consistency verified using power triangle relationships. The curated dataset is provided in tabular (CSV) format, with each record including a timestamp, three-phase voltages, three-phase currents, active and reactive power (per phase and total), power factor (per phase and global), and system frequency. This dataset offers a comprehensive characterization of electrical behavior in a university laboratory over a nine-month period. It is openly available for reuse and can support research in power system analysis, renewable energy integration, demand forecasting, energy efficiency, and the development of machine learning models for smart energy applications.

This study addresses the multi-objective optimization of an electric racing vehicle’s performance by identifying the optimal combination of battery type, pilot weight, and power mode to maximize travel time and distance while minimizing energy consumption. A sequential methodology integrating Response Surface Methodology (RSM) and the Desirability Function Approach (DFA) was applied using a 2×3×3 factorial experimental design. Performance data were collected through a custom Arduino-based acquisition system and analyzed using first- and second-order models. Results showed that the configuration comprising the smallest battery (1.0), the lowest weight (66 kg), and the lowest power mode (N5) produced the best overall performance, with a maximum operational time of 1.12 h, a travel distance of 24.63 km, and an energy efficiency of 2.90 km/Ah. The statistical models demonstrated strong explanatory power, with adjusted R² values above 0.83 and global p-values below 0.01. Both individual response optimization and global desirability analysis converged on the same optimal settings, validating the robustness of the methodology. The findings offer a replicable framework for optimizing high-efficiency electric vehicles and contribute to the broader development of sustainable transportation technologies.

This paper presents a detailed fifth-order mathematical model of a three-phase induction motor, used in the WECC (Western Electricity Coordinating Council) composite load model. The main objective is to analyze the dynamic behavior of three-phase induction motors during fault-induced delayed voltage recovery (FIDVR) events, a critical phenomenon in power system voltage stability. The model is implemented in MATLAB/Simulink and validated by comparing its results with previous studies, demonstrating its ability to capture transient and subtransient responses under voltage disturbances. Different types of motors (A, B, and C) are analyzed, and variations in load and voltage disturbance magnitudes are studied. The results show that the model is accurate and robust, making it useful for voltage stability studies in systems with high induction motor penetration. However, the model has limitations, such as the lack of consideration for frequency drives and protection systems, suggesting areas for future research.

La transformación energética se vuelve vital en la batalla contra el cambio climático, y su esencia radica en el avance de los sistemas de almacenamiento de energía. En este escenario, las baterías de litio, los volantes de inercia y las bobinas superconductoras han emergido como tecnologías fundamentales, desempeñando un papel vital en la recolección y administración de energía proveniente de fuentes renovables. Este artículo se centra en comparar estos sistemas desde un punto de vista dinámico y matemático, explorando sus modelos y características. Adicionalmente, este artículo examina la capacidad de carga, descarga, eficiencia y otros factores clave para entender sus similitudes y diferencias. La metodología incluye modelos dinámicos para cada tecnología y sección de resultados y discusión sobre su comportamiento en diferentes modos de operación. El artículo concluye resaltando la relevancia de estas tecnologías y sus posibles aplicaciones en el contexto colombiano.

The multi-objective optimization of an electric prototype racing vehicle is addressed in this study. The goal was to identify the optimal combination of battery type, pilot weight, and power mode to maximize operational time and distance while minimizing energy consumption. A structured $2\times 3\times 3$ factorial design was implemented, and the resulting data were analyzed through Response Surface Methodology (RSM) in combination with the Desirability Function Approach (DFA). The experimental design included two battery configurations, three weight levels, and three power settings, while data acquisition was performed through a custom Arduino-based system validated against commercial instruments. The results revealed that the configuration with the smallest battery, the lowest weight (66 kg), and the lowest power mode (N5) achieved the most efficient performance, yielding an operating time of 1.12 h, a travel distance of 24.63 km, and an energy performance index of 2.90 km/Ah. The integration of RSM with DFA provided a robust framework for identifying optimal multiparameter conditions under competition constraints. Unlike previous studies that examined these variables in isolation, this work advances the state of the art by demonstrating the feasibility of multiparameter optimization in real-world racing contexts, offering methodological and practical insights for sustainable electric mobility.

This paper presents a detailed mathematical procedure for implementing a three-phase induction motor model. Induction motors are crucial components in electric power systems. Therefore, several investigations have been conducted on the three-phase motor model. However, the mathematical representations for model development are complex due to nonlinearity and complicated structure. In this case, a composite load model is used to provide a block diagram based on the fifth-order analytical method in polar coordinates to calculate the transient and subtransient responses of a three-phase motor. Specifically, the behavior of a three-phase deep-slot squirrel cage motor is represented using parameters to describe its response from start-up to the voltage sag. Simulation was used to calculate the responses of the electromechanical variables after disturbances, in order to better understand the dynamic behavior of motor loads in voltage stability in electrical systems. The results were compared with those in the literature to verify the performance of the model.