Modelado neuro–borroso de un sistema captador solar lineal tipo Fresnel como gemelo digital

En este trabajo se presenta un modelado basado en un sistema de inferencia neuro-borroso adaptativo (en inglés: Adaptative Neuro-Fuzzy Inference System, ANFIS) del sistema captador solar linealde Fresnel que se encuentra en la Escuela Técnica Superior de Ingenieríade la Universidad de Sevilla, dicho sistema forma parte de una planta de refrigeración por absorción, proporcionando la fuente de calor para la misma. Su funcionamiento se basa en reflejar la radiación solar incidente en filas de espejos móviles, concentrándola en un tubo receptor de vacío por donde circula el fluido caloportador. Sobre el tubo receptor se sitúa un espejo reflector secundario, encargado de reflejar la radicación solar que no incide directamente sobre el tubo, optimizando así la eficiencia óptica del sistema. La energía recogida por el tubo receptor se transfiere entonces a un fluido caloportador (agua a presión), el cual es utilizado para accionar la máquina de absorción que transforma la energía térmica en frio, aplicación para la cual ha sido destinada en la ETSI en épocas de calor. También se puede cambiar su funcionamiento para la producción de calor en el invierno. El ANFISes implementado en el marco de redes adaptativas, el enfoque neuro-borroso integra las ventajas de las redes neuronales (Neural Networks, NN) y lógica borrosa(Fuzzy Logic, FL) para diseñar una arquitectura. Se hace uso de la FL para representar el conocimiento de manera interpretable y la capacidad de aprendizaje de una NN, para optimizar sus parámetros. Un ANFIS puede construir un mapeo de entrada-salida basado tanto en el conocimiento humano (en forma de reglas) como en pares de datos de entrada-salida dados, mostrando resultados significativos en el modelado de funciones no lineales. En este trabajo se utiliza ANFIS para modelar el sistema captador lineal Fresnelantes citado. El objetivo es obtener un modelo neuro-borroso que describa el comportamiento de la temperatura de salida del sistema de captación solar Fresnel, teniendo en cuenta las variables que infieren en dicho proceso (irradiancia solar, caudal, temperatura ambiente, temperatura del fluido que ingresa en el sistema de captación y la hora del día, que determinará la posición del Sol); para lo cual se ha usado datos de entrenamiento y comprobación, que no son necesariamente los mismos, de hecho para que elANFIS obtenga un modelo aceptable, debe trabajar preferentemente con distintos datos, tanto para el entrenamiento como para la comprobación, con el fin de captar de mejor manera la dinámica del sistema. El modelo desarrollado se ha comparado con datos reales de la planta, asícomo con los datos obtenidos por el modelo de parámetros distribuidos realizado por un trabajo previo por otro estudiante; esto con elfin de ver la eficacia del modelo obtenido.Se presenta también un estado del arte de losDigital Twins [9]conocidos como gemelos digitales(Digital Twins, DT), que muestra el concepto y evolución desde su aparición. Se puede decir que los DT tiene como principal característica la perfecta integración del espacio virtual físico y su base fundamental de desarrollo es el modelado y simulación. Los investigadores que se desempeñan en el desarrollo proponen varias arquitecturas de modelado DT: la primera arquitectura general estándar que aparece se modela en tres dimisiones: la entidad física, el modelo virtual y la conexión que se caracteriza porla interacción físicovirtual (un enlace para el flujo de datos desde el espacio real al espacio virtual). Seguidamente existe una arquitectura de cinco dimensiones, que es una extensión de la arquitectura de tres dimensiones, pero se añade una dimensión de datos y servicios; esta nueva arquitectura fusiona datos de los aspectos físicos y virtuales para una captura de información más completa y precisa que se logra usando los datos del DT. Así mismo existe una arquitectura de ocho dimisiones que describe el comportamiento y el contexto de los DT, cuatro dimensiones centradas en el contexto y el entorno, cuatro dimensiones centradas en comportamiento y la riqueza de las capacidades de los DT. En su mayoría los Digital Twin tienen en común tres partes principales: producto físico, producto virtual y datos conectados que vinculan y conectan indisolublemente el producto físico y virtual, es así como cualquier información que pueda obtenerse al inspeccionar un producto fabricado físicamente puede obtenerse de su DT.Como se ha mencionado anteriormente una de las componentes principales del DT es el modelo del equipo virtual, siendo este un modelo digital de alta fidelidadde la entidad física, que representa el modelado de la geometría, modelado de las propiedades físicas, modelado del comportamiento y el modelado de reglas en el mundo virtual; se hace hincapiéal modelado de reglas ya que posee las reglas de restricciones, asociaciones entre parámetros y las deducciones (predicciones) del comportamiento de la entidad física, es decir las reglas funcionan como el cerebro para hacer que el equipo virtual juzgue, evalúe, optimice y/o prediga.Es por ello que una de las mejores formasde modelar este tipo de conocimiento, por lo anteriormente dicho, sería utilizando un ANFIS ya que, además de poseer la capacidad de aprendizaje, su estructura está formada por reglas que forman su base de conocimiento.In this work, we present a modeling based on an adaptive neuro-fuzzy inference system (ANFIS) of the Fresnel solar line collector system located at the School of Engineering of the University of Seville. This system is part of a cooling plant by absorption, providing the heat source for it. Its operation is based on reflecting the incident solar radiation in rows of mobile mirrors, concentrating it in a vacuum receiver tube where the heat transfer fluid circulates. A secondary reflecting mirror is placed on the receiving tube, which reflects the solar radiation that does not directly affect the tube, thus optimizing the optical efficiency of the system. The energy collected by the receiver tube is then transferred to a heat-bearing fluid (pressurized water), which is used to drive the absorption machine that transforms thermal energy into cold, an application for which it has been designed at ETSI in hot periods. Its operation can also be changed to produce heat in the winter. ANFIS is implemented in the framework of adaptive networks, the neuro-fuzzy approach integrates the advantages of neural networks (Neural Networks, NN) and fuzzy logic (Fuzzy Logic, FL) to design an architecture. FL is used to represent knowledge in an interpretable way and the learning capacity of a NN, to optimize its parameters. An ANFIS can build an input-output mapping based on both human knowledge (in the form of rules) and given input-output data pairs, showing significant results in the modeling of non-linear functions. In this work, ANFIS is used to model the linear Fresnel sensor system mentioned above. The objective is to obtain a neuro-diffuse model that describes the behavior of the output temperature of the Fresnel solar collector system, taking into account the variables that are inferred in this process (solar irradiance, flow rate, ambient temperature, temperature of the fluid entering the collector system and the time of day, which will determine the position of the Sun); For which training and testing data have been used, which are not necessarily the same, in fact for ANFIS to obtain an acceptable model, it must preferably work with different data, both for training and for testing, in order to better capture the dynamics of the system. The model developed has been compared with real data from the plant, as well as with the data obtained by the distributed parameters model made by a previous work by another student; this in order to see the effectiveness of the model obtained. It also presents a state of the art of Digital Twins (DT) [9], which shows the concept and evolution since its inception. It can be said that the main feature of DTs is the perfect integration of virtual physical space and its fundamental basis of development is the modeling and simulation. The researchers involved in the development propose several DT modeling architectures: the first general standard architecture that appears is modeled in three dimensions: the physical entity, the virtual model and the connection that is characterized by the physical-virtual interaction (a link for the data flow from the real space to the virtual space). Then there is a five-dimensional architecture, which is an extension of the three-dimensional architecture, but a data and services dimension is added; this new architecture merges data from the physical virtual aspects for a more complete and accurate information capture that is achieved using the data from the DT. There is also an eight dimensional architecture that describes the behavior and context of the DTs, four dimensions focused on context and environment, four dimensions focused on behavior and the richness of the DTs' capabilities. Most of the Digital Twins have three main parts in common: physical product, virtual product and connected data that link and connect indissolubly the physical and virtual product. This is how any information that can be obtained when inspecting a physically manufactured product can be obtained from its DT. As mentioned above, one of the main components of the DT is the virtual team model, which is a high-fidelity digital model of the physical entity, representing the modeling of geometry, modeling of physical properties, modeling of behavior and modeling of rules in the virtual world; the modeling of rules is made incapable since it possesses the rules of constraints, associations between parameters and the deductions (predictions) of the behavior of the physical entity, that is to say, the rules function as the brain to make the virtual team judge, evaluate, optimize and/or predict. xv That is why one of the best ways to model this type of knowledge, for the above, would be using an ANFIS because, in addition to possessing the ability to learn, its structure is formed by rules that form its knowledge base.Universidad de Sevilla. Máster en Ingeniería Electrónica, Robótica y Automátic

MPC with fuzzy modelling for energy management in a manufacturing plant

[Resumen] Se propone un Control Predictivo Basado en Modelo (MPC) para maximizar el uso de energías renovables en un proceso de fabricación. La estrategia se ha aplicado en un sistema de fabricación que cuenta con varias máquinas, recursos de generación renovable, un generador combinado de calor y electricidad (CHP) para la producción de energía, y un banco de baterías para el almacenamiento de energía. El trabajo pretende maximizar el uso de fuentes de energía renovables en este proceso, teniendo en cuenta también el precio del mercado eléctrico, para reducir el coste. El uso de modelos neuroborrosos para la predicción de la energía producida por los generadores renovables permite una predicción dinámica, utilizando valores de entrada obtenidos a partir de variables típicas de predicción (velocidad del viento, irradiancia global, etc.).[Abstract] A Model Predictive Control (MPC) is proposed to maximise the use of renewable energy in a manufacturing process. The strategy has been applied in a manufacturing system with several machines, renewable generation resources, a combined heat and power (CHP) generator for energy production, and a battery bank for energy storage. The work aims to maximise the use of renewable energy sources in this process, also taking into account the electricity market price, to reduce the cost. The use of neuroborrous models for the prediction of the energy produced by renewable generators allows a dynamic prediction, using input values obtained from typical prediction variables (wind speed, global irradiance, etc.).Ministerio de Ciencia e Innovación; PID2019-104149RB-I0

Updating digital twins: Methodology for data accuracy quality control using machine learning techniques

The Digital Twin (DT) constitutes an integration between cyber and physical spaces and has recently become a popular concept in smart manufacturing and Industry 4.0. The related literature provides a DT characterisation and identifies the problem of updating DT models throughout the product life cycle as one of the knowledge gaps. The DT must update its performance by analysing the variable data in real time of the physical asset, whose behaviour is constantly changing over time. The automatic update process involves a data quality problem, i.e., ensuring that the captured values do not come from measurement or provoked errors. In this work, a novel methodology has been proposed to achieve data quality in the interconnection between digital and physical spaces. The methodology is applied to a real case study using the DT of a real solar cooling plant, acting as a learning decision support system that ensures the quality of the data during the update of the DT. The implementation of the methodology integrates a neurofuzzy system to detect failures and a recurrent neural network to predict the size of the errors. Experiments were carried out using historical plant data that showed great results in terms of detection and prediction accuracy, demonstrating the feasibility of applying the methodology in terms of computation time

Data Fault Detection for Digital Twin Learning Action Decision of a Wind Turbine

This paper presents the design of a classifier of variable failures in a wind turbine system. The classifier is based on a structure formed by several TS fuzzy inference systems, with projections of the data onto components of a principal component analysis. The classifier is part of a discrepancy evaluator for triggering the learning mechanism of the digital twin of the wind turbine

Modelling of a TCP-100 parabolic trough field using Simulink

[Resumen] Hoy en día, a la hora de estudiar el rendimiento de una planta, es típico contar con un modelo fiable que permita realizar pruebas sin desperdiciar recursos en ensayos innecesarios en la planta real. Uno de los programas empleados en la industria es Matlab®, el cual posee la herramienta llamada Simulink®, con la que se puede describir un sistema mediante diagramas de bloques. En este trabajo se describe el diseño de un modelo en Simulink, de la instalación de investigación de colectores cilindro-parabólicos (CCP) TCP-100, en la Plataforma Solar de Almería (PSA). Este modelo permitirá el aprendizaje didáctico sobre control de plantas termosolares mediante el uso de controladores básicos y muy usados en la industria, así como las ventajas y desventajas del uso de los mismos en plantas altamente no lineales. Los resultados del modelo en Simulink serán comparados con los obtenidos al programar el mismo modelo en el código de Matlab.[Abstract] When studying the behaviour of a plant, it is essential to have a reliable model that allows tests to be carried out without having to spend resources on unnecessary tests on the real plant. One of the programs used in the industry is Matlab, which has a tool called Simulink, with which a system can be described by means of a block diagram. This paper describes how a Simulink model of the TCP100 parabolic trough collector (PTC) research facility at the Almeria solar platform has been designed in Simulink. The comparison between the Matlab model and the Simulink model will be shown.Los autores agradecen a la Comisión Europea la financiación de este trabajo en el marco del proyecto DENiM. Este proyecto ha recibido financiación del programa de investigación e innovación Horizonte 2020 de la Unión Europea en virtud del acuerdo de subvención nº 958339 (Proyecto DENiM). Y también a el Consejo Europeo de Investigación en el marco de la subvención de investigación avanzada OCONTSOLAR (789051) The authors thanks to the European Commission for funding this work under project DENiM. This project has received funding from the European Union’s Horizon 2020 research and innovation programmeunder grant agreement No 958339 (DENIM project), and also from the European Research Council under the OCONTSOLAR Advanced Research Grant (789051).Consejo Europeo de Investigación; OCONTSOLAR 78905

Neuro-fuzzy Modelling of a Linear Fresnel-type Solar collector System as A Digital Twin

One of the main components of a Digital Twin is the modeling of the virtual entity, being this a high-fidelity digital model of the physical entity that represents the modeling of geometry, modeling of physical properties, modeling of behavior, and modeling of rules in the virtual world. This paper presents a model, based on an Adaptive Neuro-Fuzzy Inference System, of a Fresnel linear solar collector system as a Digital Twin, located on the roof of the School of Engineering of the University of Seville, which is a part of an absorption cooling plant. A distributed parameter model of the system has been used to generate artificial data. Real operating data were used to validate the model.Horizonte 2020 (Unión Europea) 95833

Wind Turbine Active Fault Tolerant Control Based on Backstepping Active Disturbance Rejection Control and a Neurofuzzy Detector

Wind energy conversion systems have become an important part of renewable energy history due to their accessibility and cost-effectiveness. Offshore wind farms are seen as the future of wind energy, but they can be very expensive to maintain if faults occur. To achieve a reliable and consistent performance, modern wind turbines require advanced fault detection and diagnosis methods. The current research introduces a proposed active fault-tolerant control (AFTC) system that uses backstepping active disturbance rejection theory (BADRC) and an adaptive neurofuzzy system (ANFIS) detector in combination with principal component analysis (PCA) to compensate for system disturbances and maintain performance even when a generator actuator fault occurs. The simulation outcomes demonstrate that the suggested method successfully addresses the actuator generator torque failure problem by isolating the faulty actuator, providing a reliable and robust solution to prevent further damage. The neurofuzzy detector demonstrates outstanding performance in detecting false data in torque, achieving a precision of 90.20% for real data and 100% for false data. With a recall of 100% , no false negatives were observed. The overall accuracy of 95.10% highlights the detector’s ability to reliably classify data as true or false. These findings underscore the robustness of the detector in detecting false data, ensuring the accuracy and reliability of the application presented. Overall, the study concludes that BADRC and ANFIS detection and isolation can improve the reliability of offshore wind farms and address the issue of actuator generator torque failure

Neuro-Fuzzy Digital Twin of a High Temperature Generator

Solar absorption plants are renewable energy systems with a special advantage: the cooling demand follows the solar energy source. The problem is that this plant presents solar intermittency, phenomenological complexity, and nonlinearities. That results in a challenge for control and energy management. In this context, this paper develops a Digital Twin of an absorption chiller High Temperature Generator (HTG) seeking accuracy and low computational efort for control and management purposes. A neuro-fuzzy technique is applied to describe HTG, internal Lithium-Bromide temperature, and water outlet temperature. Two Adaptative Neuro-Fuzzy Inference Systems (ANFIS) are trained considering real data of eight days of operation. Then, the obtained model is validated considering two days of real data. The validation shows a RMSE of 1.65e−2 for the internal normalized temperature, and 2.05e−2 for the outlet normalized temperature. Therefore, the obtained Digital Twin presents a good performance capturing the dynamics of the HTG with adaptive capabilities considering that each day can update the learning step

Digital twin of a Fresnel solar collector for solar cooling

This work develops digital entities of a commercial Fresnel Solar Collector (FSC) installed in an absorption cooling plant. The objective is to create and validate models that describe the FSC dynamics across its whole operation range during the day and the night. Thus, the temperatures range between operation temperature of 180° C and almost ambient temperature due to overnight heat losses. In the same sense, the flow range between zero to 13m³/h . The idea is that the digital twin will aid start-up and shut-down optimization and control design reliability. The paper employs two modeling approaches, then evaluates their twinning/adaptation time and performance validation. One model uses phenomenological modeling through Partial Differential Equations (PDE) and parameters identification, and another uses a data-driven technique with Adaptive Neuro-Fuzzy Inference Systems (ANFIS). The available measurement data sets comprise 25 days of operation with a sampling time of 20 s which, after outlier removal, filtering and treatment, resulted in 108416 samples. The validation considers six separate operating days. Results show that both models can twinning/adapt considering measured data. The models present pretty good results and are suitable for control and optimization. Besides, this is the first paper considering the FSC mirror defocus action on dynamic modeling and validation