In the present work, conducted as part of an internship at Siemens Gamesa Renewable Energy, optical fiber accelerometers based on Bragg gratings were developed with the aim of structural health monitoring of wind turbine blades. The dissertation begins with a description of the context of global and local energy consumption, as well as the motivation for this dissertation. In addition to a theoretical introduction to fiber optic Bragg gratings and the main methods used for their inscription, the state of the art regarding different geometries, sensitivities, and characteristics (such as the presence of metallic elements) of optical fiber sensor-based accelerometers is presented. An approach to optical accelerometers available on the market is also presented, highlighting some key parameters that should be considered in the process of selecting a device. The typical structure of wind turbine blades is described, as well as some fundamental concepts, such as the types of damage typically observed, which allow for the subsequent analysis of the tests carried out. Due to the wide versatility of geographical locations where a wind turbine can be installed and the consequent environmental variability, it is important to understand the response of the sensors used to these environmental parameters, so that these effects can be compensated. Thus, through the characterizations performed, a self-compensated temperature sensitivity of (3.6 ± 0.6) pm/ºC and a self-compensated relative humidity sensitivity of (1.6 ± 0.1) pm/%RH were obtained, confirming that these parameters can be neglected. For an operating frequency range of 0 to 30 Hz, an acceleration sensitivity of (77 ± 2) pm/G was achieved. To evaluate and verify the sensor's performance in a real context, tests were conducted in a controlled environment with a composite sample of identical characteristics to a blade, where its free-end length was varied to obtain different frequencies within the desired measurement range. Other parameters, such as the sensor's position or the excitation method, were also tested. In the final phase, tests were conducted on an SG170 blade where an intervention for repairs was carried out. As a result, a discrepancy was observed between the simulated results and the experimental results obtained, which was attributed to the damages and repairs present in the blade.

With the vast majority of scientists agreeing that the only hope in mitigating the adverse effects of climate change is to drop our carbon emissions to net zero by 2050, the decarbonization of the electricity sector is an environmental emergency. Wind energy can be a leader in the energy transition to a carbon emission-free economy. However, the wind energy transition must be carefully implemented to mitigate the economic, environmental, and social consequences of this change. Blade waste from end-of-life wind turbines is the Achilles’ heel of this energy transition and the main impediment to its full acceptance. Aiming to support efficient blade waste management and therefore to ensure sustainable wind energy transition, we conduct a two-fold methodology. In the first part, we propose a novel conceptual framework of upcycling and downcycling end-of-life solutions in an urban regeneration setting. In the second part, we use the case study method to illustrate the aspects of our conceptual framework by analyzing real life case studies. This study suggests that end-of-life blades are used in the cement coprocessing of waste and in architectural projects under urban regeneration transformation processes, closing the material loop according to the circular economy and sustainability principles.

In this work has been discussed a propose of using three-dimensional point-tracking measuring technique for measurement of large structures in which the entire field of interest could not be captured by a unique stereo-vision system. Two pairs of a stereo-system are used to capture the whole field of measurement of interest of the model and point cloud registration techniques are exploited aiming at extending the capability of digital image measurement system for dynamic measuring (displacement) for large-scale structures. Three different image registration algorithms, principle component analysis, singular value decomposition, and iterative closest point are used in the stitching process to join the point clouds obtained with the multi-camera system. The proposal is applied to vibration measurement of a wind turbine blade of 2.3 m in length, whose field of view of the whole set of points of interest is greater than the field of view of a unique stereo-vision system. The reconstruction of the set of measured points was obtained from the junction of the points clouds of each stereo-system in a reference system and a probabilistic and statistical analysis of the error generated by the transformation of the point clouds was performed. And finally, the frequencies of the structure obtained from the digital image measured data were compared with the values obtained from a set of accelerometers.

With the considerable increase in energy demand, we seek for environmentally friendly alternatives for generating complementary power. Among these alternatives, wind energy emerges, exploiting the mechanical energy created by the wind for conversion into electricity. Projects aimed at improving the structure of wind turbines have become a focal point in the area. These range from the shape of the blades to the materials from which they were manufactured. In this work, the interest lies in the materials obtained from polyhydroxybutyrate-hydroxyvalerate (PHB-HV), a biopolymer obtained from sugar cane. The objective was to study the properties of PHB-HV reinforced with nanoparticles, also of natural origin. We used the PHB-HV reinforced with eucalyptus and Cloisite® 30B clay nanofibers in different concentrations (20%, 30%, 50% and 70%), compared to the mass of PHB-HV, in order to obtain a reinforced biocomposite with potential for use in manufacturing small wind turbine blades. The samples presented inadequate use conditions, given that the manufacturing process was not efficient in dispersing the nanoparticles uniformly onto the matrix.

A energia eólica, com sua rápida expansão global, destaca-se como uma das fontes de energia renovável de crescimento mais rápido. Com os avanços na tecnologia das turbinas eólicas, houve um aumento notável no tamanho das pás, melhorando sua eficácia na captação do vento. O setor da energia eólica está ativamente explorando tecnologias inovadoras de inspeção para mitigar riscos humanos e minimizar custos. Com os avanços em robótica e técnicas de inspeção, algoritmos de aprendizado automática e inteligência artificial estão sendo desenvolvidos para automatizar e processar eficientemente grandes volumes de dados, reduzindo custos e riscos de segurança relacionados ao perigoso trabalho humano necessário para inspecionar as pás das turbinas eólicas. Em linha com esse objetivo, a presente dissertação de mestrado visa desenvolver e/ou melhorar uma ferramenta de inteligência artificial capaz de processar e detetar defeitos em imagens RGB das pás de turbinas eólicas. Conclui-se que as redes neurais convolucionais se destacam no processo de classificação de imagens em grande escala. Consequentemente, um modelo de rede neural convolucional é proposto e posteriormente treinado. Três diferentes técnicas de treino são exploradas e comparadas na capacidade de previsão geral do modelo final. Apesar do desempenho promissor para bancos de dados mais volumosos, o modelo não conseguiu treinar efetivamente no conjunto de dados de danos das pás de turbina disponível, principalmente devido ao tamanho limitado do banco de dados. Uma tentativa subsequente de desenvolver outra arquitetura CNN adaptada para bancos de dados menores também produziu resultados insatisfatórios.

Ice accumulation on blade surfaces severely limits the normal operation of wind turbine generators. At present, commercially available technologies to mitigate this problem consist of expensive, energy hungry heating elements. An alternative is the implementation of icephobic coatings. The icephobicity of a surface can be quantified by measuring how strongly ice adheres to it. This simple statement implies rather complex tasks: creating artificial ice on a surface and removing the ice from the surface in a measurable and reproducible way. In the present research, a comprehensive study was conducted to characterize ice adhesion strength over various surfaces by using a custom-built shear strength tester. The studied surfaces include both laboratory developed and commercially available coatings. In addition, the contact angles for all the surfaces were also measured to correlate the wettability and the measured ice adhesion, that is, hydrophobicity with the icephobicity. Furthermore, two numerical simulations were performed to identify the measurement uncertainties of the ice adhesion test model and to analyse the effects of the aerodynamical forces that operate in a commercial turbine. The main results drawn from this work are that there is no direct relationship between icephobicity and hydrophobicity and that icing prevention on wind turbine blades by coating alone is not realistically enough. The alternative that best applies is the combination of an active ice mitigation system with the application of these coatings.

With the recent growth in size and complexity of wind turbine blades, continuous monitoring has become imperative in reducing costs and downtime by preventing difficult maintenance and repair works. Using ambient excitation, dynamic responses are analysed with Vibration-based Structural Health Monitoring (VSHM) techniques to obtain modal parameters such as natural frequencies, mode shapes and damping ratios. For that, this study uses two output-only modal identification methods to identify those modal parameters from numerical and experimental responses, aiming to detect changes in those parameters attributed to damage. Using a cantilever beam, damage is introduced as a localized change in mass where, in the presence of damage, natural frequencies may present a decrease in value. Given the limitations of placing physical sensors on wind turbine blades, specially the ones in-service, this study uses measured responses from the structure and estimated ones at unmeasured locations, using data estimation techniques to create virtual sensors. The present objectives are to compare two modal identification methods and two response estimation ones, with the goal of understanding whether the use of virtual sensors improves damage detectability. The numerical comparison between the modal identification methods showed that both are capable of identifying modal parameters close to each other and to the numerical model ones. The results from both methods indicate that using virtual responses increases damage detectability for the same set of measurements. One of the methods provides more consistent results but requires higher computational efforts, whereas the other is much faster and simpler to use, although its results present slight variations. The comparison between the response estimation methods showed that, even with limitations, bothwere capable of identifying close modal parameters to each other. The experimental application validated the conclusions found with the numerical results, from all the methods’ behaviour to the enhancement of damage detectability.