Artículos (Ingeniería Electrónica)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11357

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MPC-Based Sliding Mode Control of Dual-Inertia System Analysis
(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Luo, Wensheng; Li, Haofei; Zhang, Ruifang; Zhang, Jianwen; Vázquez Pérez, Sergio; León Galván, José Ignacio; Wang, Xing; García Franquelo, Leopoldo; Ingeniería Electrónica; National Natural Science Foundation of China; Provincial Natural Science Foundation of Heilongjiang; The China Postdoctoral Science Foundation; Heilongjiang Postdoctoral Fund; Fundamental Research Funds for the Central Universities; National Key Laboratory of Laser Spatial Information Foundation
The servo drive system serves as the core power unit in high-end equipment such as industrial robots and computerized numerical control (CNC) machine tools, where mechanical resonance and shaft torque ripple induced by elastic deformation and backlash severely degrade motion accuracy and system stability. Conventional resonance suppression approaches, predominantly based on PI control and notch-filter-augmented PI control, suffer from critical limitations: high sensitivity to resonant frequency variations, inability to systematically enforce physical shaft torque constraints, poor robustness against parameter uncertainties and external disturbances, and significant degradation of dynamic performance when resonance is aggressively suppressed. This paper establishes a two-inertia elastic system model to investigate the effects of elastic deformation and backlash nonlinearities, revealing the mechanisms of mechanical resonance and torque ripple, and proposes control strategies for resonance suppression and shaft torque ripple limitation. A novel hierarchical control architecture is designed, consisting of a Luenberger-observer-based model predictive control (MPC) speed controller, and a super-twisting sliding mode controller (ST-SMC) for the current loop. Luenberger observer-based MPC with ST-SMC strategy is to simultaneously obtain: (a) enhanced robustness via state estimation, (b) superior dynamic performance via SMC, and (c) guaranteed shaft torque constraint satisfaction via MPC. Compared with conventional PI control and notch-filter-based PI control, simulation results demonstrate that Luenberger observer-based MPC with ST-SMC strategy effectively suppresses resonance, limits shaft torque ripple, and enhances the system’s disturbance rejection capability.
Control strategies for alkaline water electrolysis hydrogen production: a comprehensive review and future perspectives
(Elsevier, 2026-02) Dong, Zihang; Shen, Xiaojun; Wei, Li; Iranzo Paricio, José Alfredo; León Galván, José Ignacio; Ingeniería Energética; Ingeniería Electrónica; Natural Science Foundation of Shanghai
Driven by the global energy transition and carbon neutrality targets, alkaline water electrolysis has emerged as a key technology for coupling variable renewable generation with clean hydrogen production, offering considerable potential for absorbing surplus power and enhancing grid flexibility. However, conventional control architectures typically treat the power converter and electrolyzer as independent units, neglecting their dynamic interactions and thereby limiting overall system performance under practical operating conditions. This review critically examines existing control approaches, ranging from classical proportional-integral schemes to model predictive control, fuzzy-logic algorithms, and data-driven methods, evaluating their effectiveness in managing dynamic response, multivariable coupling, and operational constraints as well as their inherent limitations. Attention is then focused on the performance requirements of the hydrogen-production converter, including current ripple suppression, rapid transient response, adaptive thermal regulation, and stable power delivery. An integrated co‑control framework is proposed, aligning converter output with electrolyzer demand across steady-state operation, variable renewable input, and emergency shutdown scenarios to achieve higher efficiency, extended equipment lifetime, and enhanced operational safety. Finally, prospects for advancing unified control methodologies are outlined, with emphasis on constraint-aware predictive control, machine-learning-enhanced modeling, and real‑time co‑optimization for future alkaline electrolyzer systems.
AI-driven detection of tiny pests in foliage: Integrating image processing and deep learning
(Elsevier, 2026-03) Baeza Moreno, Lucía; Blanco-Carmona, Pedro; Hidalgo Fort, Eduardo; Martín Clemente, Rubén; González Carvajal, Ramón; Ingeniería Electrónica; Teoría de la Señal y Comunicaciones; European Union (UE); Ministerio de Industria, Turismo y Comercio. España
We present a novel computer vision method for detecting insect pests on plant and tree leaves under real-world conditions, combining deep learning with classical image processing techniques. Detecting small, sparsely distributed, or camouflaged insects is challenging, as current state-of-the-art object detection methods, primarily designed for larger objects, often overlook them. Our approach to this problem is twofold. First, we employ a deep learning model to analyze suspicious leaves for anomalies (a task well suited to deep learning). However, since deep models struggle with tiny objects in complex backgrounds, we complement them with conventional image processing to pre-identify potentially infested foliage, guiding the model toward relevant areas and mitigating its limitations. This combined strategy proves effective and competitive with other methods across diverse datasets and real-world scenarios. Furthermore, we also conduct a detailed analysis to interpret the model’s predictions, strengthening confidence in its effectiveness.
Flexible laboratory setup for DAC experimentation
(2026) Pérez Vega-Leal, Alfredo; Garrido Satué, Manuel; Ingeniería de Sistemas y Automática; Ingeniería Electrónica; TIC275: Investigación y Desarrollo en Electrónica, Automática y Tecnologías Emergentes
Analog multiplexing appears to be a promising solution for modern trans mitters, where speed is the primary limitation. The objective is the devel opment of a low-cost solution to compare different digital to analog (DAC) schemes. In particular, analog multiplexing techniques, high-speed single DAC, Sigma-delta modulation, Dynamic element matching are considered. The work presents a review of these techniques and shows a prototype of a time interleaved sigma delta modulation based DAC based on a commercially available Field Programmable Gate Array system
A new data weighted averaging algorithm to reduce tones in the signal band
(2025) Laguna García, Marta; Martínez Heredia, Juana María; Garrido Satué, Manuel; Ingeniería de Sistemas y Automática; Ingeniería Electrónica; TIC275: Investigación y Desarrollo en Electrónica, Automática y Tecnologías Emergentes
Digital/Analog converters based on sigma-delta modulation are simple and unexpensive circuits featuring a signal bandwidth limited by speed constraints. Multi-bit modulators allow balancing complexity and speed by reducing the clock frequency and increasing the number of levels in the quantizer. In this case, the multi-bit digital to analog block (DAC) can reduce the performance of the entire system. Data Weighted Averaging (DWA) methods have been proposed to reduce the vulnerability to DAC errors at the cost of spurious tones in the signal band. This work analyzes the tone producing mechanism and proposes a modification of the DWA to remove spurious tones.
A Scalable Microservices Architecture for Condition Monitoring and State-of-Health Tracking in Power Conversion Systems
(Multidisciplinary Digital Publishing Institute (MDPI), 2026-02) García-Campos, José Manuel; Márquez Alcaide, Abraham; Letrado Castellanos, Alejandro; Portillo Guisado, Ramón Carlos; León Galván, José Ignacio; Ingeniería Telemática; Ingeniería Electrónica; Ministerio de Ciencia e Innovación (MICIN). España; European Union (UE); Shanghai Partner Research Program
The role of power converters in modern electrical infrastructure (such as electric vehicle charging stations, battery energy storage systems and photovoltaic energy systems) has become critical. Given the high reliability required by these converters, continuous condition monitoring for predictive maintenance is mandatory. Traditional SCADA and HMI systems often face scalability bottlenecks and lack the flexibility in data aggregation and storage scalability required for long-term predictive maintenance. This paper proposes a scalable, containerized microservices-based architecture for degradation tracking and State-of-Health (SoH) monitoring in power conversion systems. The architecture features a decoupled four-layer structure, utilizing dedicated UDP servers for low-latency data ingestion, RabbitMQ (AMQP) for robust message routing, and a NoSQL (MongoDB) storage layer with a FastAPI interface. The proposed system was validated using a Hardware-in-the-Loop (HiL) setup with a Typhoon HIL606 simulator monitoring an Active Neutral Point Clamped (ANPC) power converter. Experimental stress tests demonstrated a Packet Delivery Ratio (PDR) of 1.0 at ingestion rates up to 100 messages per second (msgs/s) per node. The system exhibits transmission and processing overheads consistently below 5 ms, ensuring timely data availability for tracking thermal dynamics and parametric aging trends. This operational performance significantly exceeds the nominal requirement of 2 msgs/s for condition monitoring, ensuring robust data integrity. Finally, this modular approach provides the horizontal scalability necessary for Industry 4.0 integration, offering a high-performance framework for long-term health monitoring in modern power electronics.
Multi-Chiller Plant Under Demand Uncertainties: Predictive Versus Planned Approaches
(Multidisciplinary Digital Publishing Institute (MDPI), 2026-02) Garrido Satué, Manuel; Pérez Vega-Leal, Alfredo; Martínez Heredia, Juana María; Arahal, Manuel R.; Ingeniería de Sistemas y Automática; Ingeniería Electrónica
Recently, different techniques have been proposed for the scheduling and loading problems in cooling plants with chillers in a parallel configuration. Two broad groups can be considered: the online control-based group and the offline optimization-based group. The first group is exemplified by Model Predictive Control, where the selection of control moves provides a solution to both scheduling and loading. The second group includes Optimal Chiller Loading and Optimal Chiller Sequencing algorithms. They usually derive operating plans with some lead time in a batch-like fashion for long horizons. Both groups use forecasts of important factors such as the cooling demand and ambient conditions; hence, they have to deal with inaccuracies in the forecasts. In this paper, a comparison among these two groups is made considering demand uncertainties. The severity of the uncertainty is shown to play a role in the results as well as the controller tuning in the case of the predictive approach. The results are favorable to OCS with respect to overall consumption (up to 15%) but uses more on/off changes in the chiller’s operation (double in some cases).
Analog Time Multiplexing in SDM DAC
(2026-02-27) Martínez Heredia, Juana María; Pérez Vega-Leal, Alfredo; Ingeniería Electrónica; TIC275: Investigación y Desarrollo en Electrónica, Automática y Tecnologías Emergentes
The signal bandwidth of Digital to Analog Converters based on Sigma Delta Modulation is limited by speed constrains. Time-Interleaving allows coping with complexity vs. speed by replacing the original architecture by M parallel paths. These path are clocked at a frequency M times smaller and their digital outputs time multiplexed. This is then converted to analog by means of a Digital to Analog Converter clocked at the high rate. This preprint proposes that time multiplexing be performed in the analog domain. As a result robustness against dynamic effects is achieved.
Design of Dual-Band Multistandard Subsampling Receivers for Optimal SNDR in Nonlinear and Interfering Environments
(Institute of Electrical and Electronics Engineers, 2014-01) García Oya, José Ramón; Kwan, Andrew; Ghannouchi, Fadhel M.; Aidin Bassam, Seyed; Muñoz Chavero, Fernando; Ingeniería Electrónica; Ministerio de Ciencia e Innovación (MICIN). España; TIC192: Ingeniería Electrónica
This paper presents a design approach to concurrent dual-band subsampling receivers for wireless certification and testing interoperability. The proposed technique allows the optimization of the signal-to-noise and distortion ratio and improves the subsampling receiver performance, usually limited by the clock jitter and folded thermal noise effects. Furthermore, the proposed design approach considers the presence of spurious nonlinear distortions and/or interferer's signals, which can alias over the desired signal bandwidth and reduce the valid subsampling frequency ranges. The proposed architecture allows seamless reconfigurability for simultaneous multistandard signal reception, reducing the costs in test equipment. As a validation of the proposed design methodology, a dual-band multiple clocking subsampling receiver operating in a nonlinear environment is developed, and an algorithm to find the optimal frequency plan is proposed and validated experimentally.
Data Acquisition System based on Subsampling Using Multiple Clocking Techniques
(Institute of Electrical and Electronics Engineers, 2021-08) García Oya, José Ramón; Muñoz Chavero, Fernando; Torralba Silgado, Antonio Jesús; Jurado, A.; Márquez, F.J.; López Morillo, Enrique; Ingeniería Electrónica; TIC192: Ingeniería Electrónica
This paper presents the implementation of a data acquisition system, where the folded thermal noise is reduced by using two consecutive subsampling processes. The presented implementation is used to test wideband multistandard receivers covering most of present communication standards. The proposed system converts a 20-MHz signal modulated with a programmable carrier frequency up to 6.5 GHz, so that it could be used as a universal receiver for software-defined radio applications. Experimental results show an effective number of bits larger than 9 bits up to 2.9 GHz, 8 bits up to 6.5 GHz, and 6.4 bits up to 20 GHz of input carrier frequency.
Data Acquisition System based on Subsampling for Testing Wideband Multi-Standard Receivers
(Institute of Electrical and Electronics Engineers, 2011-04) García Oya, José Ramón; Muñoz Chavero, Fernando; Torralba Silgado, Antonio Jesús; Jurado, A.; Garrido, A.J.; Baños, J.; Ingeniería Electrónica; TIC192: Ingeniería Electrónica
In this paper, a data acquisition module meeting the specifications of a wideband multistandard receiver test system is presented. It provides a high resolution over large bandwidth with only a low-jitter wideband sample-and-hold and an intermediate frequency analog-to-digital converter by means of subsampling. Using commercial devices on a multilayer printed circuit board, experimental results showed more than a resolution of 8 b for a signal bandwidth of 20 MHz with a center frequency of up to 4 GHz, which is enough to cover the requirements of test systems for most of present wireless communication standards.
FVM: A Formal Verification Methodology for VHDL Designs
(Institute of Electrical and Electronics Engineers (IEEE), 2025-10) Guzmán-Miranda, Hipólito; López García, Marcos; Urbón Aguado, Alberto; Ingeniería Electrónica
With the increasing complexity of digital designs, functional verification is becoming unmanageable. Bugs that survive verification cause a number of issues with functional, performance, security, safety and economic impact, and are unfortunately prevalent in current FPGA and ASIC designs, manifesting in later stages of development or even after the design has been deployed or manufactured. In this context, Formal Verification poses itself as a powerful complement to verification by simulation, which is currently the most extended verification method. By mathematically proving properties of the designs, Formal Verification allows to verify them with high confidence, but also requires designers to have deep expertise of the methods, techniques and tools. Thus, adoption of formal methods for verification is not as extended as their usefulness may suggest, and even less in the case of VHDL teams. To lower the adoption barriers for formal verification of digital designs, the present article proposes a Formal Verification Methodology, which is complemented by a build and test framework and a repository of examples. Results of applying the Formal Verification Methodology to the repository of examples show compelling results both in manageable design complexity and verification productivity.
Modulation Analysis of Monovector and Multivector Predictive Control of Five-Phase Drives
(Multidisciplinary Digital Publishing Institute (MDPI), 2026-01) Garrido Satué, Manuel; Martínez Heredia, Juana María; Mora Jiménez, José Luis; Ingeniería Electrónica; Ingeniería de Sistemas y Automática
The Finite State Model Predictive Control (FSMPC) of variable speed drives is the subject of many works in the recent literature. Many variants of FSMPC exist, each aiming at an aspect such as the complexity of the cost function, switching frequency, current quality, etc. In the case of multiphase drives, two popular variants are the monovector and multivector techniques. Despite past efforts to compare different techniques, the field must still reach a consensus regarding the relative merits of each one. This paper presents a new method to compare two families of FSMPC. The method is based on a reduced set of figures of merit using the current modulation index as the variable. The comparison is made for the equal usage of the power converter in terms of commutations. The results point to better values for the figures of merit for the monovector that, in addition, portrays more flexibility and better DC link usage.
Computational-Efficient IGBT and Diode Thermal Modelling Methodology With High Accuracy
(Wiley, 2025) Alosa, Ciro; Márquez Alcaide, Abraham; Stowhas-Villa, Alejandro; Berger, Jhonattan G.; Immovilli, Fabio; Rojas, Christian A.; Lizana F, Ricardo; Buticchi, Giampaolo; Kouro Renaer, Samir; León Galván, José Ignacio; Ingeniería Electrónica; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Ministerio de Hacienda y Función Pública. España; Junta de Andalucía
In an industrial context where high reliability is an increasingly important requirement, thermal modelling of powersemiconductors becomes necessary for diagnostics and prognostics. This paper proposes a simulation-based methodology thatcan estimate junction temperatures in insulated gate bipolar transistors and diodes much faster than conventional methods withan improved accuracy. The approach is based on a combination of conventional steady state simulation techniques with a post-processing stage. The analysis is carried out by first calculating the conduction and switching losses and then obtaining thejunction temperature by using the device thermal network. The obtained results (including both simulations and experiments)are compared to state-of-the-art methods, highlighting the accuracy of the proposed method.
Model Reference Adaptive Predictive Current Control of Six-Phase Induction Machine
(IEEE, 2025) Arahal, Manuel R.; Garrido Satué, Manuel; Barrero, Federico; Martínez Heredia, Juana María; Ingeniería de Sistemas y Automática; Ingeniería Electrónica; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
Predictive stator current control (PSCC) is a flexible technique that has been the subject of research in connection with multiphase drives. The flexibility that the cost function (CF) brings finds an obstacle in CF tuning. Intensive trial and error tests are usual in this context. In this article, an adaptive procedure is proposed based on the concept of model reference adaptive control. Unlike previous methods, the proposal provides on-line tuning of PSCC with very little burden. The proposal is motivated by the idea of using optimal weighting factors (WFs) for each operating point. A case study is developed for a six-phase induction machine. The adaptive method includes cross terms and momentum to cope with irregularities in the derivatives with respect to the WF. It is shown that adaptiveness allows crossing the Pareto front of performance indicators. This provides flexibility without optimality loss. The proposal is assessed with real experimentation on a laboratory set-up.
Model-Free Predictive Control of Inverter Based on Ultra-Local Model and Adaptive Super-Twisting Sliding Mode Observer
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Luo, Wensheng; Shu, Zejian; Zhang, Ruifang; León Galván, José Ignacio; Márquez Alcaide, Abraham; García Franquelo, Leopoldo; Ingeniería Electrónica; National Natural Science Foundation. China; Provincial Natural Science Foundation. Heilongjiang; Ministerio de Ciencia e Innovación (MICIN). España; Junta de Andalucía
Model predictive control (MPC) is significantly affected by parameter mismatch in inverter applications, whereas model-free predictive control (MFPC) avoids parameter dependence through the ultra-local model (ULM). However, the traditional MFPC based on the algebraic method needs to store historical data for multiple cycles, which results in a sluggish dynamic response. To address the above problems, this paper proposes a model-free predictive control method based on the ultra-local model and an adaptive super-twisting sliding mode observer (ASTSMO). Firstly, the effect of parameter mismatch on the current prediction error of conventional MPC is analyzed through theoretical analysis, and a first-order ultra-local model of the inverter is established to enhance robustness against parameter variations. Secondly, a super-twisting sliding mode observer with adaptive gain is designed to estimate the unknown dynamic terms in the ultra-local model in real time. Finally, the superiority of the proposed method is verified through comparative validation against conventional MPC and the algebraic-based MFPC. Simulation results demonstrate that the proposed method can significantly enhance robustness against parameter variations and shorten the settling time during dynamic transients.
A Comparative Study of BERT-Based Models for Teacher Classification in Physical Education
(MDPI, 2025-09-28) Martín-Hoz, Laura; Yanes Luis, Samuel; Huerta Cejudo, Jerónimo; Gutiérrez Reina, Daniel; Franco Álvarez, Evelia; Tecnología Electrónica; Ingeniería Electrónica; Junta de Andalucía; TIC201: ACE-TI
Assessing teaching behavior is essential for improving instructional quality, particularly in Physical Education, where classroom interactions are fast-paced and complex. Traditional evaluation methods such as questionnaires, expert observations, and manual discourse analysis are often limited by subjectivity, high labor costs, and poor scalability. These challenges underscore the need for automated, objective tools to support pedagogical assessment. This study explores and compares the use of Transformer-based language models for the automatic classification of teaching behaviors from real classroom transcriptions. A dataset of over 1300 utterances was compiled and annotated according to the teaching styles proposed in the circumplex approach (Autonomy Support, Structure, Control, and Chaos), along with an additional category for messages in which no style could be identified (Unidentified Style). To address class imbalance and enhance linguistic variability, data augmentation techniques were applied. Eight pretrained BERT-based Transformer architectures were evaluated, including several pretraining strategies and architectural structures. BETO achieved the highest performance, with an accuracy of 0.78, a macro-averaged F1-score of 0.72, and a weighted F1-score of 0.77. It showed strength in identifying challenging utterances labeled as Chaos and Autonomy Support. Furthermore, other BERT-based models purely trained with a Spanish text corpus like DistilBERT also present competitive performance, achieving accuracy metrics over 0.73 and and F1-score of 0.68. These results demonstrate the potential of leveraging Transformer-based models for objective and scalable teacher behavior classification. The findings support the feasibility of leveraging pretrained language models to develop scalable, AI-driven systems for classroom behavior classification and pedagogical feedback.
Kalman Filter-Based Model-Free Predictive Control of Classical DC–DC Power Converters
(IEEE, 2025) Maureira, Angel; Riffo, Sebastián; Ibáñez, Esteban; González-Castaño, Carolina; Rivera, Marco; Guarnizo-Lemus, Cristian; Márquez Alcaide, Abraham; Restrepo, Carlos; Ingeniería Electrónica; Ministerio de Hacienda y Función Pública. España
Conventional model predictive control (MPC) of power converters has been widely found in many power electronics and motor drive applications. The performance of MPC strongly depends on the precision of the converter’s physical parameters, and a mismatch of them produces a control degradation, which leads to MPC suboptimal operation. Ensuring a precise estimation of the converter’s parameters is difficult because they continuously change during the operation process due to their operating point and aging. Recently, model-free predictive control (MF-PC) has been used in motor drives and power electronics converters, especially inverters and rectifiers, to deal with the predictive control method’s dependency model. However, MF-PC proposed for dc–dc converters is an open innovation scientific field. This article proposes an MF-PC designed for second-order dc–dc converters, such as the boost, buck, buck–boost, and noninverting buck–boost converters. The presented approach uses a Kalman filter to estimate the positive and negative inductor current slopes with high accuracy and a low computational cost. The experimental results show that the proposed method is robust against parameter and model changes compared to conventional model-based solutions.
eDNA and Citizen Science Reveal Hidden Fish Biodiversity in Climate-Stressed Urban Ports of the Mediterranean Sea
(Wiley, 2025) Madon, Bénédicte; Haderlé, Rachel; Arotcharen, Emma; David, Romain; Fontaine, Quentin; Marengo, Michel; Thomas, Hélène; Torralba Silgado, Antonio Jesús; Valentini, Alice; Jung, Jean-Luc; Teoría de la Señal y Comunicaciones; Ingeniería Electrónica; European Union (UE). H2020
This paper provides a pioneering study case on monitoring fish biodiversity in ports through the eDNA and citizen science approach. eDNA samples were collected in the spring and in fall 2022 in the ports of Calvi, L'Île-Rousse, STARESO, Saint-Florent. Samples collected led to the identification of 73 taxa. These ports appeared to harbor at least 20% of the known teleost biodiversity in Corsica and 11% of the Mediterranean teleost biodiversity. The ports of Calvi and L'Île-Rousse displayed the highest taxonomic, phylogenetic, and functional diversities and appeared the most similar. However, taxonomic turnover highlighted that none of the 4 ports was a subset of any of the others. In August 2022, an extreme climate event (ECE) struck Corsica, offering a unique opportunity to collect data under abnormal conditions. Although it is not possible to distinguish the seasonal effect from the ECE effect in the fall, we detected in all ports but Saint-Florent an increase in taxonomic richness, phylogenetic, and functional diversity: we did not only detect new species but also showed that these species led to an increase in the local representativeness of phylogenetic diversity, most likely correlated with new functional traits. The port of Saint-Florent displayed the highest relative phylogenetic diversity, that is, a smaller but evolutionarily more distinct group of species. Our study demonstrated the robustness and relevance of eDNA citizen science coupled with relevant indicators for port biodiversity monitoring and emphasized the need for more research and targeted conservation efforts to better understand and mitigate the ecological impacts of ports while exploring their potential as habitats.
Optical Fiber Performance for High Solar Flux Measurements in Concentrating Solar Power Applications
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Jerez González, Manuel; Carballar Rincón, Alejandro; González-Aguilar, José; Ingeniería Electrónica; Ministerio de Ciencia e Innovación (MICIN). España
Extreme operating conditions in solar receivers of concentrated solar thermal power plants, such as high temperatures, intense irradiance, and thermal cycling, pose significant challenges for conventional sensors. Optical fibers offer a promising alternative for flux measurement in such environments, but their long-term performance and degradation mechanisms require detailed investigation and characterization. This work presents a proof of concept for high solar flux measurement by using optical fibers as photon-capturing elements and showcases the behavior and damage that these optical fibers undergo when exposed to relevant conditions, including temperatures over 600 ◦C and flux levels exceeding 400 kW/m2. Three fiber configurations, including polyimide and gold-coated fibers, were tested at a high-flux solar simulator and analyzed via scanning electron microscopy to assess structural integrity and material degradation. Results reveal significant coating deterioration, fiber retraction, and thermal-induced stress effects, which impact measurement reliability. These findings provide essential insights for improving the durability and accuracy of optical fiber-based sensing technologies in concentrating solar energy.

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