Diseño de circuito de media tensión para alimentar el corregimiento de San José de Oriente (Cesar)

Ingeniería EléctricaThe distribution of electricity is vital in the process from the generation to commercialization of the same. That is where it becomes the voltage level from medium to low voltage, it means, allowing the transformation of energy to the last use by users. The design of a medium-voltage circuit is performed to improve the power distribution system or to encompass lands not energize nowadays. This work provides a detailed design level of a medium voltage circuit to the village of San Jose de Oriente located in the north of the department of Cesar which has consistently had problems with the quality of electricity service due to voltage fluctuations generated by the long distances required by the circuit that energizes the whole region now. This design is based on current legislation NTC 2050 and RETIE for constructions of overhead distribution lines.La distribución de la energía eléctrica es de vital importancia en el proceso que va desde la generación hasta la comercialización de la misma. Es en ella donde se transforma el nivel de tensión de media a baja tensión, es decir, es el escalafón que permite la utilización final de la energía por parte de los usuarios. El diseño de un circuito de media tensión se realiza para mejorar el sistema de distribución de energía o para abarcar terrenos no energizados hasta el momento. El presente trabajo de grado ofrece un diseño detallado de un circuito de media tensión para el corregimiento de San José de Oriente ubicado al norte del departamento de Cesar que ha tenido constantemente inconvenientes con la calidad del servicio de energía eléctrica debido a fluctuaciones de tensión que se generan por los largos recorridos que debe realizar el circuito que actualmente energiza toda la región. Dicho diseño se basa en la normativa vigente NTC 2050 y RETIE para construcción de líneas aéreas de distribución

Recent trends of the most used metaheuristic techniques for distribution network reconfiguration

Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for large distribution networks, which requires large computational times. For solving this type of problem, some researchers prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics applied to DNR in order to continue developing new best algorithms and improving solutions for the topi

Solar radiation prediction for dimensioning photovoltaic systems using artificial neural networks

This paper presents a prediction model of solar radiation for dimensioning photovoltaic generation systems in the Atlantic Coast of Colombia, using artificial neural networks. As a case of study is presented the municipality "El Carmen de Bolivar" located in this region. To obtain the model, the average data of daily temperature, relative humidity and solar radiation from the last ten years, reported by weather stations in this city were used. Six neural networks were designed with six variants of input variables (temperature, humidity and month) and the output variable (solar radiation). The best result was obtained using all input variables. In the training process, the correlation index (R) between solar radiation estimated by the model and the recorded data was 0.8. In validating the correlation index was 0.77