DEP32 - Artículos de revistaDpto. Física de la Materia Condensada, Cristalografía y Mineralogía - Artículos de revistahttps://uvadoc.uva.es/handle/10324/12422024-03-29T09:10:04Z2024-03-29T09:10:04ZMicro-Fracture Detection in Photovoltaic Cells with Hardware-Constrained Devices and Computer VisionSerrano Gutierrez, JorgeFaasen, Booy VitasRosero-Montalvo, Paúl D.https://uvadoc.uva.es/handle/10324/666132024-03-12T20:02:51Z2024-01-01T00:00:00ZSolar energy is rapidly becoming a robust renewable energy source to conventional finite resources such as fossil fuels. It is harvested using interconnected photovoltaic panels, typically built with crystalline silicon cells, i.e. semiconducting materials that convert effectively the solar radiation into electricity. However, crystalline silicon is fragile and vulnerable to cracking over time or in predictive maintenance tasks, which can lead to electric isolation of parts of the solar cell and even failure, thus affecting the panel performance and reducing electricity generation. This work aims to developing a system for detecting cell cracks in solar panels to anticipate and alaert of a potential failure of the photovoltaic system by using computer vision techniques. Three scenarios are defined where these techniques will bring value. In scenario A, images are taken manually and the system detecting failures in the solar cells is not subject to any computationa constraints. In scenario B, an Edge device is placed near the solar farm, able to make inferences. Finally, in scenario C, a small microcontroller is placed in a drone flying over the solar farm and making inferences about the solar cells' states. Three different architectures are found the most suitable solutions, one for each scenario, namely the InceptionV3 model, an EfficientNetB0 model shrunk into full integer quantization, and a customized CNN architechture built with VGG16 blocks.
2024-01-01T00:00:00ZTemperature dependence of the Raman spectrum of orthorhombic Bi2Se3Serrano Gutiérrez, JorgeJiménez López, Juan IgnacioMediavilla Martínez, IreneAyala, PaolaPichler, ThomasKramberger, ChristianMuñoz, AlfonsoManjón, Francisco JavierRodríguez Hernández, PlácidaBuga, SergeiSerebryanaya, NadezhnaDadgostar, Shabnamhttps://uvadoc.uva.es/handle/10324/663022024-02-19T20:16:11Z2004-01-01T00:00:00ZBismuth selenide, a benchmark topological insulator, grows in a trigonal structure at ambient conditions and exhibits a number of enticing properties related to the formation of Dirac surface states. Besides this polytype, a metastable orthorhombic modification with Pnma space group has been produced by electrodeposition and high-pressure high-temperature synthesis displaying upon Sb doping significant thermoelectric properties in the midtemperature range. However, very little experimental information is available on the fundamental properties of this polytype, such as, e.g., the electronic band gap and the lattice dynamics. We report here the temperature dependence of the Raman spectra of orthorhombic Bi2Se3 between 10 K and 300 K, which displays an anharmonic behavior of the optical phonons that can be modelled with a two-phonon decay channel. In order to analyze the data we performed ab initio calculations of the electronic bandstructure, the phonon frequencies at the center of the Brillouin zone, and the phonon dispersion relations along the main symmetry directions, examining the effect of spin-orbit coupling in both phonon and electronic energies. Lastly, we report here cathodoluminescence experiments at 83 K that set a lower limit to the electronic bandgap at 0.835 eV, pointing to an indirect nature, in agreement with our calculations. These results shed light to essential properties of orthorhombic Bi2Se3 for further understanding of the potential of this semiconductor for thermoelectrics and new applications.
2004-01-01T00:00:00ZPhotovoltaic Cells Defects Classification by Means of Artificial Intelligence and Electroluminescence ImagesMateo-Romero, Héctor FelipePérez-Romero, ÁlvaroHernández-Callejo, LuisGallardo-Saavedra, SaraAlonso-Gómez, VíctorMorales-Aragonés, José IgnacioPlaza, Alberto RedondoMartínez, Diego Fernándezhttps://uvadoc.uva.es/handle/10324/660072024-02-08T20:01:43Z2022-01-01T00:00:00ZMore than half of the total renewable addictions correspond to solar photovoltaic (PV) energy. In a context with such an important impact of this resource, being able to produce reliable and safety energy is extremely important and operation and maintenance (O&M) of PV sites must be increasingly intelligent and advanced. The use of Artificial Intelligence (AI) for the defects identification, location and classification is very interesting, as PV plants are increasing in size and quantity. Inspection techniques in PV systems are diverse, and within them, electroluminescence (EL) inspection and current-voltage (I-V) curves are one of the most important. In this sense, this work presents a classifier of defects at the PV cell level, based on AI, EL images and cell I-V curves. To achieve this, it has been necessary to develop an instrument to measure the I-V curve at the cell level, used to label each of the PV cells. In order to determine the classification of cell defects, CNNs will be used. Results obtained have been satisfactory, and improvement is expected from a greater number of samples taken.
2022-01-01T00:00:00ZEnhancing Solar Cell Classification Using Mamdani Fuzzy Logic Over Electroluminescence Images: A Comparative Analysis with Machine Learning MethodsMateo-Romero, Hector FelipeRosa, Mario Eduardo Carbonó delaHernández-Callejo, LuisGonzález-Rebollo, Miguel ÁngelCardeñoso-Payo, ValentínAlonso-Gómez, VictorGallardo-Saavedra, Sarahttps://uvadoc.uva.es/handle/10324/660002024-02-08T20:01:42Z2024-01-01T00:00:00ZThis work presents a Mamdani Fuzzy Logic model capable of classifying solar cells according to their energetic performance. The model has 3 different inputs: The proportion of black pixels, gray pixels, and white pixels. One additional output for informing of possible bad inputs is also provided. The three values are obtained from an Electroluminescence image of the cell. The model has been developed using cells whose performance has been obtained by measuring the Intensity-Voltage Curves of the cells. The performance of the model has been shown by testing it with a validation set, obtaining a 99.0% of accuracy, when other methods such as Ensemble Classifiers and Decision Trees obtain a 97.7%. This shows that the presented model is capable of solving the problem better than traditional Machine Learning methods.
2024-01-01T00:00:00ZEstimation of the Performance of Photovoltaic Cells by Means of an Adaptative Neural Fuzzy Inference ModelMateo-Romero, Hector FelipeRosa, Mario Eduardo Carbonó delaHernández-Callejo, LuisGonzález-Rebollo, Miguel ÁngelCardeñoso-Payo, ValentínAlonso-Gómez, VictorMartínez-Sacristán, ÓscarGallardo-Saavedra, Sarahttps://uvadoc.uva.es/handle/10324/659982024-02-08T20:01:40Z2024-01-01T00:00:00ZThis paper presents an Adaptive Neuro-fuzzy Inference System capable of predicting the output power of photovoltaic cells using their electroluminescence image and their IV curve. The input consists of 3 different features: the number of black pixels, grey pixels and white pixels. ANFIS combines the learning capabilities of Artificial Neural Networks with the comprehensible rules of Fuzzy Logic, being optimal for this problem, as demonstrated by the metrics of MAE of 0.064 and MSE of 0.009, which are better than the performance of other tested methods such as Support Vector Machines or Linear Regressor.
2024-01-01T00:00:00ZApplications of Artificial Intelligence to Photovoltaic Systems: A ReviewMateo Romero, Héctor FelipeGonzález Rebollo, Miguel ÁngelCardeñoso-Payo, ValentínAlonso Gómez, VictorRedondo Plaza, AlbertoMoyo, Ranganai TawandaHernández-Callejo, Luishttps://uvadoc.uva.es/handle/10324/659952024-02-08T20:01:39Z2022-01-01T00:00:00ZThis article analyzes the relationship between artificial intelligence (AI) and photovoltaic (PV) systems. Solar energy is one of the most important renewable energies, and the investment of businesses and governments is increasing every year. AI is used to solve the most important problems found in PV systems, such as the tracking of the Max Power Point of the PV modules, the forecasting of the energy produced by the PV system, the estimation of the parameters of the equivalent model of PV modules or the detection of faults found in PV modules or cells. AI techniques perform better than classical approaches, even though they have some limitations such as the amount of data and the high computation times needed for performing the training. Research is still being conducted in order to solve these problems and find techniques with better performance. This article analyzes the most relevant scientific works that use artificial intelligence to deal with the key PV problems by searching terms related with artificial intelligence and photovoltaic systems in the most important academic research databases. The number of publications shows that this field is of great interest to researchers. The findings also show that these kinds of algorithms really have helped to solve these issues or to improve the previous solutions in terms of efficiency or accuracy.
2022-01-01T00:00:00ZEvaluation of Artificial Intelligence-Based Models for Classifying Defective Photovoltaic CellsPérez-Romero, ÁlvaroMateo-Romero, Héctor FelipeGallardo-Saavedra, SaraAlonso-Gómez, VíctorAlonso-García, María del CarmenHernández-Callejo, Luishttps://uvadoc.uva.es/handle/10324/659942024-02-08T20:01:36Z2021-01-01T00:00:00ZSolar Photovoltaic (PV) energy has experienced an important growth and prospect during the last decade due to the constant development of the technology and its high reliability, together with a drastic reduction in costs. This fact has favored both its large-scale implementation and small-scale Distributed Generation (DG). PV systems integrated into local distribution systems are considered to be one of the keys to a sustainable future built environment in Smart Cities (SC). Advanced Operation and Maintenance (O&M) of solar PV plants is necessary. Powerful and accurate data are usually obtained on-site by means of current-voltage (I-V) curves or electroluminescence (EL) images, with new equipment and methodologies recently proposed. In this work, authors present a comparison between five AI-based models to classify PV solar cells according to their state, using EL images at the PV solar cell level, while the cell I-V curves are used in the training phase to be able to classify the cells based on its production efficiency. This automatic classification of defective cells enormously facilitates the identification of defects for PV plant operators, decreasing the human labor and optimizing the defect location. In addition, this work presents a methodology for the selection of important variables for the training of a defective cell classifier.
2021-01-01T00:00:00ZSynthetic Dataset of Electroluminescence Images of Photovoltaic Cells by Deep Convolutional Generative Adversarial NetworksHernández-Callejo, LuisRebollo, Miguel Ángel GonzálezCardeñoso-Payo, ValentínGómez, Victor AlonsoBello, Hugo JoseMoyo, Ranganai TawandaAragonés, Jose Ignacio Moraleshttps://uvadoc.uva.es/handle/10324/659862024-02-08T20:01:35Z2023-01-01T00:00:00ZAffordable and clean energy is one of the Sustainable Development Goals (SDG). SDG compliance and economic crises have boosted investment in solar energy as an important source of renewable generation. Nevertheless, the complex maintenance of solar plants is behind the increasing trend to use advanced artificial intelligence techniques, which critically depend on big amounts of data. In this work, a model based on Deep Convolutional Generative Adversarial Neural Networks (DCGANs) was trained in order to generate a synthetic dataset made of 10,000 electroluminescence images of photovoltaic cells, which extends a smaller dataset of experimentally acquired images. The energy output of the virtual cells associated with the synthetic dataset is predicted using a Random Forest regression model trained from real IV curves measured on real cells during the image acquisition process. The assessment of the resulting synthetic dataset gives an Inception Score of 2.3 and a Fréchet Inception Distance of 15.8 to the real original images, which ensures the excellent quality of the generated images. The final dataset can thus be later used to improve machine learning algorithms or to analyze patterns of solar cell defects.
2023-01-01T00:00:00ZStarch-fibers composites, a study of all-polysaccharide foams from microwave foaming to biodegradationQuilez Molina, Ana IsabelLe Meins, Jean-FrançoisCharrier, BertrandDumon, Michelhttps://uvadoc.uva.es/handle/10324/658462024-02-06T20:01:56Z2024-01-01T00:00:00ZSustainable composite foams based on rice starch and cellulosic long fibers were successfully fabricated using microwave irradiation. They were presented as a promising method to recycle some of the textile industry waste. A deep study of the processability and functionality of the composites revealed the performance improvement of starch with the addition of long cellulosic fibers, especially with 6 wt% of Arbocel®, in terms of foamability, water, and mechanical resistance features. Moreover, sodium bicarbonate, which acted as a blowing and pulping agent, led to a lower density and better fiber distribution that resulted in an improvement of the foams' functionalities. The range of the study is new in the domain of long fiber foam composites in terms of the foaming capability, and mechanical, thermal, and water resistance properties. Furthermore, all foams showed excellent biodegradability properties against a fungus commonly found in the environment; for example, values around 60 % weight loss after 33 days. Finally, the assessment of the CO2 emission during the process underlines the environmental benefits of the method employed.
2024-01-01T00:00:00ZToward a Green Chemistry Approach for the Functionalization of Melamine Foams with Silver NanoparticlesQuilez Molina, Ana IsabelBarroro Solares, SusetRodríguez Pérez, Miguel ÁngelPinto Sanz, Javierhttps://uvadoc.uva.es/handle/10324/657452024-02-05T20:03:03Z2023-01-01T00:00:00ZThe growing popularity of silver nanoparticles in the field of nanotechnology has created the necessity of developing new sustainable synthesis methods. This study presents a new green in situ functionalization method of melamine foams with silver nanoparticles. The synthesis pathway and the influence of the processing parameters are optimized to phase out 100% of polluting and dangerous solvents while maximizing silver transfer. A deep study of the morphological and chemical changes of the synthesized silver nanoparticles successfully demonstrated that water can be used as the only solvent for obtaining active melamine foams with potential application in multiple fields. Results showed that rising reaction temperatures from environmental to mild conditions (40 °C and 60 °C) is crucial for obtaining high functionalization yields with this green method. Following the optimum fabrication conditions using only water, highly functionalized melamine foams showed a great amount of ultrafine silver nanoparticles distributed over the porous structure.
2023-01-01T00:00:00ZCooperative effect of chemical and physical processes for flame retardant additives in recycled ABSRodriguez, AliciaHerrero Villar, ManuelAsensio Valentín, MaríaSantiago Calvo, MercerdesGuerrero, JuliaCañibano Álvarez, EstebanFernández, Maria TeresaNuñez, Karinahttps://uvadoc.uva.es/handle/10324/655872024-02-02T20:01:05Z2023-01-01T00:00:00ZIn the present work, the effectiveness of four non-halogenated flame retardants (FR) (aluminium trihydroxide (ATH), magnesium hydroxide (MDH), Sepiolite (SEP) and a mix of metallic oxides and hydroxides (PAVAL)) in blends with recycled acrylonitrile-butadiene-styrene (rABS) was studied in order to develop a more environmentally friendly flame-retardant composite alternative. The mechanical and thermo-mechanical properties of the obtained composites as well as their flame-retardant mechanism were evaluated by UL-94 and cone calorimetric tests. As expected, these particles modified the mechanical performance of the rABS, increasing its stiffness at the expense of reducing its toughness and impact behavior. Regarding the fire behavior, the experimentation showed that there is an important synergy between the chemical mechanism provided by MDH (decomposition into oxides and water) and the physical mechanism provided by SEP (oxygen barrier), which means that mixed composites (rABS/MDH/SEP) can be obtained with a flame behavior superior to that of the composites studied with only one type of FR. In order to find a balance between mechanical properties, composites with different amounts of SEP and MDH were evaluated. The results showed that composites with the composition rABS/MDH/SEP: 70/15/15 wt.% increase the time to ignition (TTI) by 75% and the resulting mass after ignition by more than 600%. Furthermore, they decrease the heat release rate (HRR) by 62.9%, the total smoke production (TSP) by 19.04% and the total heat release rate (THHR) by 13.77% compared to unadditivated rABS; without compromising the mechanical behavior of the original material. These results are promising and potentially represent a greener alternative for the manufacture of flame-retardant composites.
2023-01-01T00:00:00ZSpin–orbit splitting of acceptor states in Si and CWysmolek, ARuf, TCardona, MSerrano Gutiérrez, Jorgehttps://uvadoc.uva.es/handle/10324/653662024-01-30T20:02:18Z1999-01-01T00:00:00ZWe report calculations of the Γ8–Γ7 spin–orbit splittings of substitutional acceptor levels in silicon and diamond and corresponding Raman measurements for Si : X (X=B, Al, Ga, In). The calculations were performed using a Green's function method based on a full-zone 30×30 k.p Hamiltonian together with a Slater–Koster ansatz for the acceptor potential. The results are in reasonable agreement with experimental data.
1999-01-01T00:00:00ZSpin–orbit splitting in diamond: excitons and acceptor related statesCardona, MRuf, TSerrano Gutiérrez, Jorgehttps://uvadoc.uva.es/handle/10324/653652024-01-30T20:02:17Z2000-01-01T00:00:00ZThe spin–orbit splitting Delta_0 approx. 13 meV calculated ab initio for the Gamma 1_8 valence band state of diamond differs from that observed for acceptors (approx. 2 meV) and exciton states (approx. 7 meV). A full-zone k·p band structure, together with a Slater–Koster attractive potential, is used to explain these differences and thus clarify the contradictory assignments of spin–orbit splittings found in the literature for diamond.
2000-01-01T00:00:00ZTheoretical study of the relative stability of structural phases in group-III nitrides at high pressuresRubio, AngelHernández, EduardoMuñoz, AlfonsoMujica, AndrésSerrano Gutiérrez, Jorgehttps://uvadoc.uva.es/handle/10324/653632024-01-30T20:02:16Z2000-01-01T00:00:00ZWe present the results of a first-principles theoretical study of the relative stability of several structural phases of the group–III nitrides AlN, GaN, and InN that complements the picture of the behavior under pressure of these technologically important materials. Along with structures which have been previously considered in other theoretical studies of these materials (comprising those of the observed phases: wurtzite, zinc-blende, and rocksalt; and the d−β−Sn, NiAs, and CsCl structures) we have also assessed the stability of several novel structures, viz., the cinnabar structure, the Cmcm structure, and the sc16 structure, which have been recently observed in high-pressure experiments on various related compounds (e.g., GaAs) and have also been reported to be either stable or close to stable in a certain range of pressures in other III-V and II-VI compounds on the basis of first-principles calculations. Our results indicate, however, that in AlN, GaN, and InN, the high-pressure rocksalt phase remains stable with respect to any other phase considered in this study up to the highest pressures investigated of ∼200 GPa, which agrees with the available experimental data. We have further considered the effect of the semicore d orbitals of GaN and InN on the phase diagram of these compounds.
2000-01-01T00:00:00ZEffect of Pressure on the Anomalous Raman Spectrum of CuBrManj�n, F.J.Loa, I.Syassen, K.Lin, C.T.Cardona, M.Serrano Gutiérrez, Jorgehttps://uvadoc.uva.es/handle/10324/653622024-01-30T20:02:15Z2001-01-01T00:00:00ZRaman spectra of isotopically pure zinc-blende CuBr (63Cu81Br) were measured at T = 10 K under hydrostatic pressures up to 4 GPa. The ambient-pressure spectrum consists of a transverse-optic (TO) phonon line and an anomalous broad band located at frequencies near the expected first-order longitudinal-optic (LO) scattering. Application of hydrostatic pressure leads to an increase of the linewidth of the TO mode and to a significant change of the LO scattering band which above 3 GPa develops into a single LO phonon peak of narrow linewidth. The disappearance of the LO Raman anomaly as well as the broadening of the TO mode with pressure are explained by pressure-dependent third-order anharmonic interactions of zone-center optical phonons with q = 0 two-phonon states.
2001-01-01T00:00:00ZPhonon Dispersion Curves in Wurtzite-Structure GaN Determined by Inelastic X-Ray ScatteringRuf, T.Cardona, M.Pavone, P.Pabst, M.Krisch, M.D'Astuto, M.Suski, T.Grzegory, I.Leszczynski, M.Serrano Gutiérrez, Jorgehttps://uvadoc.uva.es/handle/10324/653612024-01-30T20:02:14Z2001-01-01T00:00:00ZWe have investigated the lattice dynamics of a wurtzite GaN single crystal by inelastic x-ray scattering. Several dispersion branches and phonons at high-symmetry points have been measured, including the two zone-center Raman- and infrared-inactive silent modes. The experiments have been complemented by ab initio calculations. They are in very good agreement with our measurements, not only for phonon energies, but also for scattering intensities, thus validating the correctness of the eigenvectors. Other phenomenological and ab initio theories exhibit significant differences.
2001-01-01T00:00:00Z