Desarrollo de estrategias de control predictivas de alto rendimiento mediante la incorporación de técnicas de control robusto y de redes neuronales

Desarrollo de estrategias de control predictivas con mejores características en cuanto a estabilidad, carga computacional y seguimiento de consignas variables. El algoritmo de control empleado es el controlador predictivo generalizado (GPC). En primera lugar se estudian cuestiones relacionadas con la aplicabilidad del GPC para lo cual se lleva a cabo el diseño e implementación de una estrategia GPC mejorada sobre un motor de corriente continua. En segundo lugar se propone un algoritmo GPC con mejores características en cuanto a estabilidad y, por lo tanto, computacionalmente más eficaz para lo cual se emplean técnicas de control robusto. En último lugar se aborda el problema de la elección acertada de los horizontes de predicción cuando se consideran consignas variables. Para resolver este problema se plantea un esquema basado en redes neuronales que permite la sintonización autómatica del horizonte de predicción en el GP

Virtual Sensor for Fault Detection, Isolation and Data Recovery for Bicomponent Mixing Machine Monitoring

[Abstract] The present research shows the implementation of a virtual sensor for fault detection with the feature of recovering data. The proposal was implemented over a bicomponent mixing machine used for the wind generator blades manufacture based on carbon fiber. The virtual sensor is necessary due to permanent problems with wrong sensor measurements. The solution proposed uses an intelligent model able to predict the sensor measurements, which are compared with the measured value. If this value belongs to a specified range, it is valid. Otherwise, the prediction replaces the read value. The process fault detection feature has been added to the proposal, based on consecutive erroneous readings, obtaining satisfactory results

A new method for anomaly detection based on non-convex boundaries with random two-dimensional projections

[Abstract] The implementation of anomaly detection systems represents a key problem that has been focusing the efforts of scientific community. In this context, the use one-class techniques to model a training set of non-anomalous objects can play a significant role. One common approach to face the one-class problem is based on determining the geometric boundaries of the target set. More specifically, the use of convex hull combined with random projections offers good results but presents low performance when it is applied to non-convex sets. Then, this work proposes a new method that face this issue by implementing non-convex boundaries over each projection. The proposal was assessed and compared with the most common one-class techniques, over different sets, obtaining successful results

Deep learning en la predicción de generación de un parque eólico

[Resumen] Uno de los grandes retos tecnológicos actuales es la incorporación de las energías renovables al sistema eléctrico. El objetivo es conseguir que la generación eléctrica sea sostenible y respetuosa con el medioambiente, así como abordable económicamente. Sin embargo para que esta incorporación tenga éxito es necesario disponer de herramientas de predicción que permitan conocer con suficiente antelación la cantidad de energía de origen renovable que estaría disponible para ser inyectada enla red; permitiendo ajustar adecuadamente el resto de fuentes de generación con el objeto de suplir la demanda, incluidas las basadas en combustibles fósiles. Esto permitiría limitar el impacto ambiental y la dependencia con respecto este tipo de carburantes en un previsible escenario de escasez. En este trabajo se quiere avanzar en la creación de dichos modelos de predicción de la generación de los parques eólicos utilizando aprendizaje profundo o deep learning. En este artículo se presenta un modelo de predicción basado en una red neuronal profunda multicapa que, a partir de la predicción de las condiciones atmosféricas, es capaz de estimar 24 horas antes la generación producida por un parque eólico situado en la isla de Tenerife.https://doi.org/10.17979/spudc.978849749808

Short-Term Energy Demand Forecast in Hotels Using Hybrid Intelligent Modeling

This paper is the extension of the conference paper: Casteleiro-Roca, J.-L.; Gómez-González, J.F.; Calvo-Rolle, J.L.; Jove, E.; Quintián, H.; Acosta Martín, J.F.; Gonzalez Perez, S.; Gonzalez Diaz, B.; Calero-Garcia, F. and Méndez-Perez, J.A. Prediction of the Energy Demand of a Hotel Using an Artificial Intelligence-Based Model. In Proceedings of the 13th International Conference, Hybrid Artificial Intelligent Systems (HAIS), Oviedo, Spain, 20–22 June 2018.[Abstract] The hotel industry is an important energy consumer that needs efficient energy management methods to guarantee its performance and sustainability. The new role of hotels as prosumers increases the difficulty in the design of these methods. Also, the scenery is more complex as renewable energy systems are present in the hotel energy mix. The performance of energy management systems greatly depends on the use of reliable predictions for energy load. This paper presents a new methodology to predict energy load in a hotel based on intelligent techniques. The model proposed is based on a hybrid intelligent topology implemented with a combination of clustering techniques and intelligent regression methods (Artificial Neural Network and Support Vector Regression). The model includes its own energy demand information, occupancy rate, and temperature as inputs. The validation was done using real hotel data and compared with time-series models. Forecasts obtained were satisfactory, showing a promising potential for its use in energy management systems in hotel resortsFundación CajaCanarias; grant number PR70575

Anomaly detection based on intelligent techniques over a bicomponent production plant used on wind generator blades manufacturing

[Resumen] Los avances tecnológicos en general, y en el ámbito de la industria en particular, conllevan el desarrollo y optimización de las actividades que en ella tienen lugar. Para alcanzar este objetivo, resulta de vital importancia detectar cualquier tipo de anomalía en su fase más incipiente, contribuyendo, entre otros, al ahorro energético y económico, y a una reducción del impacto ambiental. En un contexto en el que se fomenta la reducción de emisión de gases contaminantes, las energías alternativas, especialmente la energía eólica, juegan un papel crucial. En la fabricación de las palas de aerogenerador se recurre comúnmente a materiales de tipo bicomponente, obtenidos a través del mezclado de dos substancias primarias. En la presente investigación se evalúan distintas técnicas inteligentes de clasificación one-class para detectar anomalías en un sistema de mezclado para la obtención de materiales bicomponente empleados en la elaboración de palas de aerogenerador. Para lograr los modelos inteligentes que permitan la detección de anomalías, se han usado datos reales extraídos de una planta de mezclado en operación durante su correcto funcionamiento. Los clasificadores obtenidos para cada técnica son validados a través de anomalías generadas de manera artificial, obteniéndose resultados altamente satisfactorios.[Abstract] Technological advances, especially in the industrial field, have led to the development and optimization of the activities that takes place on it. To achieve this goal, an early detection of any kind of anomaly is very important. This can contribute to energy and economic savings and an environmental impact reduction. In a context where the reduction of pollution gasses emission is promoted, the use of alternative energies, specially the wind energy, plays a key role. The wind generator blades are usually manufactured from bicomponent material, obtained from the mixture of two dierent primary components. The present research assesses dierent one-class intelligent techniques to perform anomaly detection on a bicomponent mixing system used on the wind generator manufacturing. To perform the anomaly detection, the intelligent models were obtained from real dataset recorded during the right operation of a bicomponent mixing plant. The classifiers for each technique were validated using artificial outliers, achieving very good results

An intelligent system for harmonic distortions detection in wind generator power electronic devices

The high concern about climate change has boosted the promotion of renewable energy systems, being the wind power one of the key generation possibilities in this field. In this context, with the aim of ensuring the energy efficiency, the present work deals with the fault detection in the power electronic circuits of a wind generator system placed in a bioclimatic house. To do so, different outliers that emulate harmonic distortion appearance are tested. To implement a system capable of detecting this anomalous situations, six different one-class techniques are used, whose performance is thoroughly analyzed, offering interesting performance.info:eu-repo/semantics/publishedVersio

Electromyogram prediction during anesthesia by using a hybrid intelligent model

[Abstract] In the search for new and more efficient ways to administer drugs, clinicians are turning to engineering tools. The availability of these models to predict physiological variables are a significant factor. A model is set out in this research to predict the EMG (electromyogram) signal during surgery, in patients under general anaesthesia. This prediction hinges on the Bispectral Index™ (BIS) and the infusion rate of the drug propofol. The results of the research are very satisfactory, with error values of less than 0.67 (for a Normalized Mean Squared Error). A hybrid intelligent model is used which combines both clustering and regression algorithms. The resulting model is validated and trained using real data.Ministerio de Innovación y Ciencia; DPI2010-1827

Experimental Techniques to Measure Hypnotic Levels During Surgery

[Abstract] The administration of anesthetics during a surgical procedure has been done historically in a manual way with the anesthesiologist deciding what amounts and at what rates to use. Over the last few decades there has been a rapid increase in the automation of many medical areas including anesthesiology, with that increased level of automation have also appeared new ways to measure the level of sedation in patients. Historically, one of the most frequently index used has been the BIS, which has proven rather reliable as an indicator. More recently, another index called PSI has attracted interest of practitioners. In this article a comparison of these two indexes was performed. Data recording BIS and PSI values from surgical operations for several patients were collected and analyzed. The results seem to indicate that it is to be expected that in 95% of the cases the correlation between the BIS and PSI index will be at least 0.6866.This work has been supported by the grant DPI2010-18278 of the Spanish Governmenthttps://doi.org/10.17979/spudc.978849749808