Digital twin of a Fresnel solar collector for solar cooling

Machado, Diogo Ortiz; Chicaiza, William D.; Escaño, Juan Manuel; Gallego, Antonio J.; Andrade, Gustavo A. de; Normey‐Rico, Julio E.; Bordons, Carlos; Camacho, Eduardo F.

doi:10.1016/j.apenergy.2023.120944

Cited by 16 publications

(4 citation statements)

References 34 publications

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“…The variables that compose matrices WT X and PV X have different scales, which can affect the learning process due to inconsistencies. It is solved by the normalization process, thus avoiding the atypical nature and magnitude of the variables, as noted in [30,32]. Hence, each variable of both matrices s X must be normalized to zero mean and unit variance to give them equal weight by…”

Section: Neurofuzzy Model Of Resmentioning

confidence: 99%

Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications

Ranade,

Gómez,

de Juan

et al. 2024

Energies

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The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope of mainstream research articles. In this paper, a comprehensive case-study detailing our collaborative partnership with a leading medical device manufacturer is presented. The study traces its evolution from a state of limited digitalisation to the development of a digital intelligence platform that leverages data and machine learning models to enhance operations across a wide range of critical machines and assets. The main business objective was to enhance the energy efficiency of the manufacturing process, thereby improving its sustainability measures while also saving costs. The project encompasses energy modelling and analytics, Fault Detection and Diagnostics (FDD), renewable energy integration and advanced visualisation tools. Together, these components enable informed decision making in the context of energy efficiency.

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Section: Neurofuzzy Model Of Resmentioning

confidence: 99%

Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications

Ranade,

Gómez,

de Juan

et al. 2024

Energies

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“…Ambos modelos neuroborrosos tienen dos reglas. Para una descripción detallada del proceso de modelado ANFIS con las proyecciones de los datos mediante PCA, consulte (Machado et al, 2023). El proceso de validación compara la salida prevista FIS s con el conjunto de datos de validación correspondiente a cada RES.…”

Section: Modelos Neuroborrosos De Las Energías Renovablesunclassified

MPC con modelos neuroborrosos para la gestión energética en una planta de fabricación

Gómez,

Chicaiza Salazar,

Escaño

et al. 2023

XLIV Jornadas De Automática: Libro De Actas: Universidad De Zaragoza, Escuela De Ingeniería Y Arquitectura, 6, 7 Y 8 De Septiem

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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.).

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“…Initially, a correlation analysis of the system data is performed according to [31]. Thus, the correlation coefficient matrix of the wind turbine data was obtained WT R ∈ 9×9 with M = 9 variables, respectively.…”

Section: Neurofuzzy Detector Based On Pca Projectionsmentioning

confidence: 99%

“…where the description of each of the parameters that make up the rule is extensively described in [31].…”

Section: Learning Process Of the Detectormentioning

confidence: 99%

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

Assia,

Merabet Boulouiha,

Chicaiza

et al. 2023

Energies

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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.

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Digital twin of a Fresnel solar collector for solar cooling

Cited by 16 publications

References 34 publications

Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications

Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications

MPC con modelos neuroborrosos para la gestión energética en una planta de fabricación

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

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