Simplified method based on an intelligent model to obtain the extinction angle of the current for a single-phase half wave controlled rectifier with resistive and inductive load

With the aim of calculating the extinction angle of the current of a single-phase half wave controlled rectifier with resistive and inductive load, present work shows a method to obtain a regression model based on intelligent methods. This type of circuit is a typical non-linear case of study that requires a hard work to solve it by hand. To create the intelligent model, a dataset has been obtained with a computational method for the working range of the circuit. Then, with the dataset, to achieve the final solution, several methods of regression were tested from traditional to intelligent types. The model was verified empirically with electronic circuit software simulation, analytical methods and with a practical implementation. The advantage of the proposed method is its low computational cost. Then, the final solution is very appropriate for applications where high computational requirements are not possible, like low-performance microcontrollers or web applications

An intelligent fault detection system for a heat pump installation based on a geothermal heat exchanger

The heat pump with geothermal exchanger is one of the best methods to heat up a building. The heat exchanger is an element with high probability of failure due to the fact that it is an outside construction and also due to its size. In the present study, a novel intelligent system was designed to detect faults on this type of heating equipment. The novel approach has been successfully empirically tested under a real dataset obtained during measurements of one year. It was based on classification techniques with the aim of detecting failures in real time. Then, the model was validated and verified over the building; it obtained good results in all the operating conditions ranges

Sistema híbrido inteligente para la predicción de tensión de una pila de combustible basada en hidrógeno

Por razones de sostenibilidad y estrategia energética, entre otras, existe en la actualidad una tendencia clara hacia el uso de nuevas formas de obtención, almacenamiento y gestión de energía, más eficientes y con un carácter eminentemente sostenible. Con este fin, se está investigando sobre sistemas de almacenamiento de energía; de los que uno de los más prometedores, en lo que a capacidad y movilidad se refiere, es el basado en hidrógeno. En el presente trabajo se obtiene un modelo para predecir el comportamiento dinámico de una pila de combustible alimentada por hidrógeno, lo cual permitirá mejorar su control entre otras aplicaciones. Las variables usadas en esta investigación se han extraído de un banco de pruebas real, donde se monitoriza una pila de combustible mientras se producen variaciones en una carga programable conectada a la salida de la misma. Para realizar este modelado se opta por estudiar la implementación de un modelo híbrido basado en técnicas de agrupamiento y, posteriormente, técnicas inteligentes de regresión con redes neuronales artificiales sobre cada uno de los grupos. La propuesta se ha probado con dos conjuntos de datos de validación, consiguiendo resultados altamente satisfactorios.Due to some reasons like sustainability and energy strategy, there is a clear trend using new ways to obtain energy, more e cient and, usually, renewables. In addition, with other di erent objectives, many researchs are being carried out on energy storage systems; one of the most promising, in terms of capacity and mobility, is hydrogen-based. In the present work a model is obtained to predict the dynamic behavior of a hydrogen fuel cell, which will improve its control. The variables used in this research have been extracted from a test bench, where a fuel cell is monitored under several load conditions with a programmable load connected to its output. To perform this model, a hybrid intelligent model was chosen. This kind of models use clustering techniques to divide the data set and, after that, intelligent regression algorithm with artificial neural networks are used for each group. The proposal has been tested with two validation data set, obtaining highly satisfactory results.Los autores de este trabajo quieren agradecer el soporte en materia de financiación del Ministerio de Economía, Industria y Competitividad del Gobierno de España a través del proyecto H2SMART- uGRID (DPI2017-85540-R)