Control predictivo no lineal de un lazo de colectores cilindro-parabólicos

JORNADAS DE AUTOMÁTICA (32) (32.2011.SEVILLA, ESPAÑA)El avance en el diseño y estudio de los sistemas de energía termosolar, ha experimentado un gran auge en los últimos 30 años. Existen varios tipos de tecnología en plantas solares, la más común es la tecnología cilindro-parabólica. En este tipo de plantas, el objetivo es controlar la temperatura de salida de un fluido, generalmente un aceite térmico, para generar energía eléctrica. Es un sistema bastante interesante desde el punto de vista del control, por sus fuertes no linealidades, así como múltiples fuentes de perturbaciones como la Irradiación, temperatura de entrada etc, lo que hace que un control lineal simple sea, en general, insuficiente. En este trabajo se propone un control predictivo no lineal que hace uso de un filtro de Kalman unscented para estimar la eficiencia global del campo, generalmente muy difícil de estimar por la cantidad de parámetros que la afectan. Esta estrategia será probada con datos tomados de la planta solar de Almería, comparándola con un control predictivo lineal con tabla de ganancias.Unión Europea DPI 2008-05818Junta de Andalucía P07-TEP-0272

Mathematical Modeling of the Mojave Solar Plants

Competitiveness of solar energy is one of current main research topics. Overall efficiency of solar plants can be improved by using advanced control strategies. To design and tuning properly advanced control strategies, a mathematical model of the plant is needed. The model has to fulfill two important points: (1) It has to reproduce accurately the dynamics of the real system; and (2) since the model is used to test advanced control strategies, its computational burden has to be as low as possible. This trade-off is essential to optimize the tuning process of the controller and minimize the commissioning time. In this paper, the modeling of the large-scale commercial solar trough plants Mojave Beta and Mojave Alpha is presented. These two models were used to test advanced control strategies to operate the plants.Comisión Europea OCONTSOLAR 78905

Mathematical Modeling of the Parabolic Trough Collector Field of the TCP-100 Research Plant

The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016 Oulu (Finlandia)There are two main drawbacks when operating solar energy systems: a) the resulting energy costs are not yet competitive and b) solar energy is not always available when needed. In order to improve the overall solar plants efficiency, advances control techniques play an important role. In order to develop efficient and robust control techniques, the use of accurate mathematical models is crucial. In this paper, the mathematical modeling of the new TCP100 parabolic trough collector (PTC) research facility at the Plataforma Solar de Almería is presented. Some simulations are shown to demonstrate the adequate behavior of the model compared to the facility design conditions.Junta de Andalucía P11-TEP-8129Unión Europea FP7-ICT-ICT-2013.3.4-611281Ministerio de Economía y Competitividadt DPI2014-56364-C2-2-

Control predictivo distribuido de un grupo caldera turbina

XXXI Jornadas Nacionales de Automática Jaén 2010En este trabajo se aborda el control de un grupo 3x3 caldera turbina, el cual es multivariable, no lineal y posee fuerte interacción entre sus variables, así como fuertes restricciones en la amplitud y variación de las señales de control. Se propone abordar el problema de una manera distribuida, asociando cada variable de control a una variable física del sistema. La estrategia a utilizar es el control predictivo, tanto por su capacidad para tratar las restricciones en la fase de diseño del controlador, como por la facilidad en extender el problema del ámbito centralizado al distribuido. La efectividad y buen comportamiento del controlador quedan demostrados en simulación, logrando buen seguimiento a referencias y error en régimen estacionario nulo tanto en pequeñas variaciones en torno a un punto de trabajo como en grandes cambios en el mismo, siempre respetando las restricciones impuestas en la señal de control. Los resultados se comparan con dos controladores basados en técnicas de optimización y control robusto, comprobándose un comportamiento similar y en algunos casos mejor.Unión Europea HD-MPC (200899900949906

Incremental State-Space Model Predictive Control of a Fresnel Solar Collector Field

Model predictive control has been demonstrated to be one of the most efficient control techniques for solar power systems. An incremental offset-free state-space Model Predictive Controller (MPC) is developed for the Fresnel collector field located at the solar cooling plant installed on the roof of the Engineering School of Sevilla. A robust Luenberger observer is used for estimating the states of the plant which cannot be measured. The proposed strategy is tested on a nonlinear distributed parameter model of the Fresnel collector field. Its performance is compared to that obtained with a gain-scheduling generalized predictive controller. A real test carried out at the real plant is presented, showing that the proposed strategy achieves a very good performance.Comisión Europea ID 78905

Modelado y control de un captador solar tipo Fresnel

XXXII Jornadas Nacionales de Automática Sevilla 2011En este artículo se presenta el modelado matemático, así como algoritmos de control de un captador solar tipo Fresnel que pertenece planta de refrigeración solar situada en la Escuela Superior de Ingenieros de la Universidad de Sevilla. Se va a desarrollar un modelo de parámetros distribuidos, ajustando los parámetros del mismo con datos tomados del sistema real y comparando la respuesta del modelo con la salida del sistema. Por último, se implementará una estrategia de control clásica, un PID con un compensador de perturbaciones de tipo feedforward paralelo, cuyo desempeño será validado mediante simulación usando el modelo de parámetros distribuidos.Unión Europea DPI 2008-05818Junta de Andalucía P07-TEP-0272

Deep Learning-Based Fault Detection and Isolation in Solar Plants for Highly Dynamic Days

ICCAD'22: 2022- 6th International Conference on Control, Automation and Diagnosis, Lisbon, Portugal, July 13-15, 2022Solar plants are exposed to numerous agents that degrade and damage their components. Due to their large size and constant operation, it is not easy to access them constantly to analyze possible failures on-site. It is, therefore, necessary to use techniques that automatically detect faults. In addition, it is crucial to detect the fault and know its location to deal with it as quickly and effectively as possible. This work applies a fault detection and isolation method to parabolic trough collector plants. A characteristic of solar plants is that they are highly dependent on the sun and the existence of clouds throughout the day, so it is not easy to achieve methods that work well when disturbances are too variable and difficult to predict. This work proposes dynamic artificial neural networks (ANNs) that take into account past information and are not so sensitive to the variations of the plant at each moment. With this, three types of failures are distinguished: failures in the optical efficiency of the mirrors, flow rate, and thermal losses in the pipes. Different ANNs have been proposed and compared with a simple feedforward ANN, obtaining an accuracy of 73.35%.European Research Council 10.13039/50110000078

Modelo en Ecosimpro® de captador solar Fresnel

XXXIII Jornadas de Automática. 05/09/2012. VigoSe ha desarrollado en este trabajo un conjunto de componentes de EcosimPro® para la simulación del captador tipo Fresnel de una planta solar situada en la Escuela Técnica Superior de Ingeniería de la Universidad de Sevilla. Se ha basado en un modelo de parámetros distribuidos, ajustando los parámetros del mismo con datos tomados del sistema real y comparando la respuesta del modelo con la temperatura de salida real del sistema.Ministerio de Eduación y Ciencia DPI2010-21589- C05-0

A deep learning-based strategy for fault detection and isolation in parabolic-trough collectors

Solar plants are exposed to the appearance of faults in some of their components, as they are vulnerable to the action of external agents (wind, rain, dust, birds …) and internal defects. However, it is necessary to ensure a satisfactory operation when these factors affect the plant. Fault detection and diagnosis methods are essential to detecting and locating the faults, maintaining efficiency and safety in the plant. This work proposes a methodology for detecting and isolating faults in parabolic-trough plants. It is based on a three-layer methodology composed of a neural network to obtain a preliminary detection and classification between three types of fault, a second stage analyzing the flow rate dynamics, and a third stage defocusing the first collector to analyze thermal losses. The methodology has been applied by simulation to a model of the ACUREX plant, which was located at the Plataforma Solar de Almería. The confusion matrices have been obtained, with accuracies over 80% when using the three layers in a hierarchical structure. By forcing all the three layers, the accuracies exceed 90%.Unión Europea - Horizonte 2020 No 789 05

Hybrid Nonlinear MPC of a Solar Cooling Plant

Solar energy for cooling systems has been widely used to fulfill the growing air conditioning demand. The advantage of this approach is based on the fact that the need of air conditioning is usually well correlated to solar radiation. These kinds of plants can work in different operation modes resulting on a hybrid system. The control approaches designed for this kind of plant have usually a twofold goal: (a) regulating the outlet temperature of the solar collector field and (b) choosing the operation mode. Since the operation mode is defined by a set of valve positions (discrete variables), the overall control problem is a nonlinear optimization problem which involves discrete and continuous variables. This problems are difficult to solve within the normal sampling times for control purposes (around 20–30 s). In this paper, a two layer control strategy is proposed. The first layer is a nonlinear model predictive controller for regulating the outlet temperature of the solar field. The second layer is a fuzzy algorithm which selects the adequate operation mode for the plant taken into account the operation conditions. The control strategy is tested on a model of the plant showing a proper performance.Unión Europea OCONTSOLAR ID 78905