DIEA-Artículos

URI permanente para esta colecciónhttps://hdl.handle.net/10953/234

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3D object recognition for anthropomorphic robots performing tracking tasks
(Springer, 2019-07-03) Satorres Martínez, Silvia; Sánchez García, Alejandro; Estévez Estévez, Elisabet; Gómez Ortega, Juan; Gámez García, Javier
Object recognition is still a major research issue of particular relevance in robotics. The new trend in industrial and mainly in service robotics is to perform manipulation tasks in an unstructured environment working in synergy with humans. To perform tasks in an environment that is not perfectly controlled, robots need adequate perceptual capabilities. Among various types of sensors available for robotic systems, the time-of-flight (ToF) camera is one of the most utilized since it simultaneously provides intensity and depth data at a high frame rate. Our proposal makes use of this technology exploiting both, depth and grey-scale information. Therefore, intensity and geometric features are fused together to allow 3D object recognition in real scenes in presence of partial occlusions. As a case study, an object tracking task for an anthropomorphic robot is presented. Experimental results demonstrate the effectiveness of the proposed method, not only providing reliable visual information about the object to be tracked but also recognizing potential obstacles which should be avoided during the robot arm movement.
The Advantage of Multispectral Images in Fruit Quality Control for Extra Virgin Olive Oil Production
(Springer, 2021-08-17) Martínez Gila, Diego M.; Navarro Soto, Javiera P.; Satorres Martínez, Silvia; Gómez Ortega, Juan; Gámez García, Javier
The highest quality of extra virgin olive oil must be guaranteed to succeed in the competitive market of the industry. To fulfil this need, high standards of fruit quality are required. In recent years, this search has led to the development of computer vision systems for the automatic inspection of fruits. Skin damage, dirtiness, and external colour were the main features selected. However, no known report is related to the internal quality evaluation of olives. The present research explores the application of multispectral images (from 600 to 975 nm) to discriminate olive fruits by their firmness. To achieve this aim, 58 olives were classified as hard or soft by an expert and then measured by a penetrometer. Afterwards, 58 multispectral images were obtained, and 8352 pixels were randomly selected for feature extraction. The feature vector of each pixel was composed of 25 infrared absorption values. These data were input into three supervised classification algorithms: a naïve Bayes (NB) classifier, a multilayer perceptron (MLP), and a support vector machine variation (SMO). The highest performance was achieved by the MLP, with an accuracy of 95% for the firmness grading. Furthermore, the three algorithms were subjected to principal component analysis (PCA), by which the MLP was confirmed to be the best model and its results were improved. Moreover, according to the second principal component, the 675 nm and 855 nm band frequencies have the strongest weights. The overall results confirm the convenience of multispectral imaging in olive firmness inspection, with the advantage of being a non-invasive technology.
Application of a Lab-Made Voltametric Electronic Tongue to Identify Musty and Vinegary Defects in Olive Oils
(Springer, 2022-11-25) Martínez Gila, Diego Manuel; Estévez Estévez, Elisabet; Gómez Ortega, Juan; Gámez García, Javier
The olive oil sector is undergoing a technological transformation promoted by high international competitiveness. This transformation must be aligned with the concepts of industry 4.0 that are already well defined and implemented in other sectors. The integration of advanced sensorics in the process phase in which the quality of the manufactured product is inspected is key to responding in the shortest possible time to deviations in the desired quality. In this work, the results of the experimentation carried out with an e-tongue voltammetric sensor are presented to evaluate its potential in the detection of two of the organoleptic defects that appear more frequently in olive oil (musty and vinegary). This sensor has been built in the research group’s laboratory and is made up of three metals in the measurement probe (nickel, silver and copper). Three classification algorithms (Support Vector Machines, Naïve Bayes and Classification Trees) were used and musty-type defect was identified with a success rate of 72%, while the vinegary-type defect was detected with a success rate of 84%.
Olive fly sting detection based on computer vision
(ELSEVIER, 2019-11-14) Beyaz, Abdullah; Martínez Gila, Diego Manuel; Gómez Ortega, Juan; Gámez García, Javier
Olive fly (Bactrocera oleae Rossi) is a parasite that infects olive fruit by stinging the fruit to deposit its eggs. The infection damages the fruit during its growth and affects the quality of the olive oil as a final product. Detection of olive fly stings in the virgin olive oil production process is therefore critical. This research aimed to develop automatic methodologies based on computer vision to detect fly stings in infected olive fruit samples. Different methodologies to detect defective areas on the surface of the fruit and classify them between stings and other bruises are described. The methodology to detect defective areas reached a success ratio of 93% and best classification algorithms reached a success ratio close to 80%. The proposed methodology could be applied at reception of fruit for input of the virgin olive oil production process.
Classification of olive fruits and oils based on their fatty acid ethyl esters content using electronic nose technology
(SPRINGER, 2021-08-20) Martínez Gila, Diego Manuel; Sanmartin, Chiara; Navarro Soto, Javiera; Mencarelli, Fabio; Gómez Ortega, Juan; Gámez García, Javier
Among several parameters defined for the commercial classes of virgins olive oils (VOOs), there is one, the fatty acid ethyl ester (FAEE), that is only define for the best quality (EVOO). Fruit condition mainly determine these compounds, although, extraction process or deplorable storage condition could rise them up. The FAEE oxidation compound are originated by adding an alcohol chain into the oil molecule. Therefore, the hypothesis of this study is that the inherent constitution of FAEE entails a modification of the volatile profile of oils and olives and this is significant enough to be detected using an electronic nose. With this aim, different samples of olives and oils were analyzed in an accredited laboratory. On the other hand, volatiles from the same samples were captured by an electronic nose. The classification problem was analyzed from two points of view or models. The first was to classify fruits and oils based on whether they are within or outside the legal limits. And the second problem was oriented to classify fruits and oils based on their high or low FAEE content but being within the legal limits. To solve this problem, three classification algorithms were evaluated: Naïve Bayes (NB), Multilayer Perceptron (MLP) and Sequential Minimal Optimization (SMO). For the first model, a well-classified sample rate of 80.3% was obtained for NB and 100% for SMO and MLP, for measurements on oils. The same model evaluated with measurements on olives yielded a success rate of 87.5% with NB, 87.7% with MLP and 82.1% with SMO. For the second model, the suc- cess rates remained within the same orders of magnitude. For measurements on oils, the results were 89.7% for NB, 92.5% for MLP and 100% for SMO. And for measurements on olives the results were 77.9% for NB, 88.6% for MLP and 90.9% for SMO. In all cases, the characteristics that worked best were those obtained from the first derivative of the electronic nose response. Based on these results, the e-nose demonstrate to be a non-invasive technology suitable for the classification of olive fruits and oils based on their FAEE content.
Fast tool based on electronic nose to predict olive fruit quality after harvest
(ELSEVIER, 2019-11-14) Martínez Gila, Diego Manuel; Gámez García, Javier; Bellincontro, Andrea; Mencarelli, Fabio; Gómez Ortega, Juan
uality analyses of oil from olive fruit are performed according to regulated procedures and in accredited la- boratories that are usually separated from the oil mill. These analytics include organoleptic features involving smelling by human experts. Therefore, oil features depend on the physicochemical conditions of the harvested fruit. An automatic and non-invasive system for monitoring and controlling the process in postharvest stages could optimize the quality of the processed oil. To validate this hypothesis we proposed a methodology based on an electronic nose sensor and pattern recognition algorithms to predict the quality of the oil to be processed from measurements on freshly harvested olive fruit. The pattern recognition algorithms applied were the Naïve Bayes (NB) classifier, the partial least squares discriminant analysis (PLSDA) and a multilayer perceptron (MLP) ar- tificial neural network. Using the measurements performed on 82 samples of olives, the best result was obtained with the MLP network, with 90.2 % success obtained in the classification of the virgin and extra virgin olive oil quality by applying 10-fold cross-validation. Integration of this methodology to virgin olive oil production allows prediction of the quality of the final oil from the olive fruit received from the farmer.
Classification of olive fruits and oils based on their fatty acid ethyl esters content using electronic nose technology
(SPRINGER, 2021-08-20) Martínez Gila, Diego Manuel; Sanmartin, Chiara; Navarro Soto, Javiera; Mencarelli, Fabio; Gómez Ortega, Juan; Gámez García, Javier
Among several parameters defined for the commercial classes of virgins olive oils (VOOs), there is one, the fatty acid ethyl ester (FAEE), that is only define for the best quality (EVOO). Fruit condition mainly determine these compounds, although, extraction process or deplorable storage condition could rise them up. The FAEE oxidation compound are originated by adding an alcohol chain into the oil molecule. Therefore, the hypothesis of this study is that the inherent constitution of FAEE entails a modification of the volatile profile of oils and olives and this is significant enough to be detected using an electronic nose. With this aim, different samples of olives and oils were analyzed in an accredited laboratory. On the other hand, volatiles from the same samples were captured by an electronic nose. The classification problem was analyzed from two points of view or models. The first was to classify fruits and oils based on whether they are within or outside the legal limits. And the second problem was oriented to classify fruits and oils based on their high or low FAEE content but being within the legal limits. To solve this problem, three classification algorithms were evaluated: Naïve Bayes (NB), Multilayer Perceptron (MLP) and Sequential Minimal Optimization (SMO). For the first model, a well-classified sample rate of 80.3% was obtained for NB and 100% for SMO and MLP, for measurements on oils. The same model evaluated with measurements on olives yielded a success rate of 87.5% with NB, 87.7% with MLP and 82.1% with SMO. For the second model, the suc- cess rates remained within the same orders of magnitude. For measurements on oils, the results were 89.7% for NB, 92.5% for MLP and 100% for SMO. And for measurements on olives the results were 77.9% for NB, 88.6% for MLP and 90.9% for SMO. In all cases, the characteristics that worked best were those obtained from the first derivative of the electronic nose response. Based on these results, the e-nose demonstrate to be a non-invasive technology suitable for the classification of olive fruits and oils based on their FAEE content.
Photovoltaic Device Performance Evaluation Using an Open-Hardware System and Standard Calibrated Laboratory Instruments
(MDPI, 2017-11-15) Jesús Montes Romero, Michel Piliougine; José Vicente Muñoz, Eduardo F. Fernández; Juan De la Casa
This article describes a complete characterization system for photovoltaic devices designed to acquire the current-voltage curve and to process the obtained data. The proposed system can be replicated for educational or research purposes without having wide knowledge about electronic engineering. Using standard calibrated instrumentation, commonly available in any laboratory, the accuracy of measurements is ensured. A capacitive load is used to bias the device due to its versatility and simplicity. The system includes a common part and an interchangeable part that must be designed depending on the electrical characteristics of each PV device. Control software, developed in LabVIEW, controls the equipment, performs automatic campaigns of measurements, and performs additional calculations in real time. These include different procedures to extrapolate the measurements to standard test conditions and methods to obtain the intrinsic parameters of the single diode model. A deep analysis of the uncertainty of measurement is also provided. Finally, the proposed system is validated by comparing the results obtained from some commercial photovoltaic modules to the measurements given by an independently accredited laboratory.
Optical Design of a 4-Off-Axis-Unit Cassegrain Ultra-High CPV Module with Central Receiver
(OSA - The Optical Society, 2016-05-01) JUAN P. FERRER-RODRÍGUEZ, EDUARDO F. FERNÁNDEZ; FLORENCIA ALMONACID, PEDRO PÉREZ-HIGUERAS
Ultra-High Concentrator Photovoltaics, UHCPV, with concentrations higher than 1000 suns, has been pointed by different authors to have a great potential for being a cost-effective PV technology. This Letter presents a UHCPV Cassegrain-based optical design in which the sunrays are concentrated and sent from four different and independent paraboloid-hyperboloid pairs optical units onto a single central receiver. The optical design proposed has as main advantage the achievement of ultra-high concentration ratios, using relative small mirrors, with similar performance values of efficiency, acceptance angle and irradiance uniformity than other designs.
Comparative assessment of the spectral impact on the energy yield of high concentrator and conventional photovoltaic technology
(Elsevier Ltd, 2016-04) Eduardo F. Fernández, Alberto Soria-Moya; Florencia Almonacid, Jorge Aguilera
Photovoltaic materials are spectrally selected and their electrical output is affected by the spectral distribution of the incident irradiance. The performance of high concentrator photovoltaic (HCPV) systems is more influenced by the spectral changes than conventional single-junction photovoltaic (PV) systems due to the use of multi-junction (MJ) solar cells and optical devices. Despite this, the detailed comparison of the spectral impact on the electrical output of HCPV and PV technology under the same atmospheric conditions has not been addressed yet. Because of this, this paper aims to compare the spectral impact on the energy yield of both type of devices at a monthly and annual time scale at several locations with disparate climate conditions. The spectral dependence of both technologies is quantified by using the spectral factor (SF) index in conjunction with the Simple Model of Atmospheric Radiative Transfer of Sunshine (SMARTS) at five locations of the Aerosol Robotic Network (AERONET) database. The present paper shows that the current HCPV systems present annual spectral losses of around 5% with respect to PV systems at representative locations.
A method for estimating the cell temperature at maximum power point of a HCPV module under actual operating conditions
(Elsevier Ltd, 2014-05) Eduardo F. Fernández, P. Rodrigo; F. Almonacid, P. Pérez-Higueras
The operating cell temperature of a HCPV module or system is a key factor, because it directly affects efficiency and reliability. Hence, the accurate estimation of cell temperature in a HCPV module is crucial. Under real operating conditions, HCPV modules work at the maximum power point, because they are connected to an inverter. At the maximum power point, the cell temperature is lower than the cell temperature of open circuit, because solar cells are generating power which is not transformed into heat. At present, none of the existing methods are valid to estimate the operating cell temperature of a HCPV module connected to an inverter. In this paper, a procedure for estimating this temperature is introduced. The results show that the proposed method performs effectively in the estimation of the cell temperature in a HCPV module connected to an inverter. In addition, an analysis of the difference between the cell temperature of open circuit and the cell temperature at the maximum power point in a HCPV is conducted and a difference of up to 21 °C has been found.
Calculation of the cell temperature of a High Concentrator Photovoltaic (HCPV) module: a study and comparison of different methods
(Elsevier Ltd, 2014-02) Eduardo F. Fernández, F. Almonacid; P. Rodrigo, P. Pérez-Higueras
Ascertaining the operating cell temperature of a high concentrator photovoltaic (HCPV) module is critical because its electrical parameters are influenced by this factor. However, measuring the cell temperature of an HCPV module is a complex task due to the unique features of such a module. This paper calculates the cell temperature in an HCPV module by using different methods to address this important issue. We conducted a comparative study of four methods used to estimate the cell temperature of an HCPV module, including the IEC 60904-5 method, a method based on thermal resistance proposed by the Instituto de Sistemas Fotovoltaicos de Concentración, the Lineal method and an artificial neural network-based method introduced in this paper. The complete procedures, parameters and coefficients required to estimate the cell temperature with each method are provided. The results show that methods based on direct measurements of the HCPV module perform better than methods based on atmospheric parameters. However, all of the studied methods can be used to estimate cell temperatures with an acceptable margin of error.
Analysis of the spectral variations on the performance of high concentrator photovoltaic modules operating under different real climate conditions
(2014-08) Eduardo F. Fernández, F. Almonacid; J. A. Ruiz-Arias, A. Soria-Moya
Multi-junction (MJ) solar cells show an important dependence on the incident spectrum due to the internal series connection of several cells with different band gap energies. The influence of spectral variations on the performance of HCPV modules or systems is different from that in MJ solar cells since they use optical devices to concentrate the light on the solar cell surface. The spectral distribution of irradiance is affected by atmospheric parameters and changes during the course of day, month or year. Because of this, several authors have done different studies to analyse and quantify the spectral effects on the performance of HCPV modules. However, there are still important issues that have not been addressed. In this paper, a deep analysis of the spectral effects on the performance of different HCPV modules with different multi-junction solar cells and Fresnel lenses on an annual time scale and their study and comparison at locations with different climate conditions is conducted. In order to address this issue, ground-based climatologies at the locations studied, spectra simulations with the SMARTS model and the spectral factor of a HCPV module have been used. Results show that the annual spectral losses vary from 6% to 51% depending on the climate conditions of the location and the HCPV module.
Investigating the impact of weather variables on the energy yield and cost of energy of grid-connected solar concentrator systems
(Elsevier Ltd, 2016-07-01) Eduardo F. Fernández, Diego L. Talavera; Florencia M. Almonacid, Greg P. Smestad
This work connects the electrical performance and economics of High Concentrator Photovoltaic technology beyond the cell and module levels. It analyses the impact of fundamental variables on the calculated energy output and economics of a typical system for real-world solar power plants in five locations with diverse climatic conditions. It was found that there exists a nearly linear relationship between the Final Energy Yield and the average direct normal irradiance, while the cell temperature and spectral AC energy losses ranged from 4.6% to 1.8% and 5.0%–2.4%. The LCOE (Levelised Cost of Electricity) calculations used these insights, together with the specific economic values for each location. The results show that the locations with the higher annual energy yield tend to have the lower LCOE values. In particular, the LCOE ranged from 5.5 c€/kWh to 22.2 c€/kWh for a conservative scenario. However, the sites with the highest final yield do not necessarily present the lowest values of LCOE. The results emphasize the interrelationship between the instantaneous effects of cell temperature and spectrum on the performance of the system, as well as the importance of considering the specific economic parameters to estimate the LCOE at each location.
Outdoor evaluation of concentrator photovoltaic systems modules from different manufacturers: first results and steps
(Wiley, 2012-01-31) Eduardo F. Fernández, Pedro Pérez-Higueras; Antonio J. Garcia Loureiro, Pedro G. Vidal
For the behavior of concentrator photovoltaic systems technology under real conditions to be understood, different modules from different manufacturers were measured in a new research center in Jaén. The influence in the power and the efficiency of irradiation levels, air temperature, and the influence of air mass were under study for 6 months. Pmax shows a linear be- havior with direct normal irradiance, and efficiency was constant to a first approximation for a wide range of irradiance levels. The effect of air temperature was negligible for the temperatures under study. At the same time, efficiency shows a maximum around AM1.5 and decreases aside this point.
Quantification of the spectral coupling of atmosphere and photovoltaic system performance: Indexes, methods and impact on energy harvesting
(Elsevier B.V., 2017-04-01) Pedro M. Rodrigo, Eduardo F. Fernández; Florencia M. Almonacid, Pedro J. Pérez-Higueras
Photovoltaic system performance is affected by changes in the input sunlight spectrum. Moreover, the different photovoltaic materials employed show different spectral responses, having different spectral behaviour as a result. Many authors have developed methods and proposed indexes for quantifying the spectral influences in photovoltaic systems under the time-varying weather variables. These methods use different equipment, different procedures and assumptions, present different levels of complexity and accuracy, and have advantages and disadvantages in each specific context and application. In this paper, for the first time, a systematic review of the available methods and photovoltaic spectral indexes is presented. Each alternative is analysed in detail and a comparative study is done. In addition, as several authors have measured and/or calculated the spectral impact on the energy yield of the different photovoltaic technologies at particular locations and climates, the existing results are summarized and discussed in order to elucidate the spectral behaviour of each technology as a function of the relevant atmospheric parameters, i.e. air mass, aerosol optical depth and precipitable water. The presented study covers non-concentrating and high-concentrating photovoltaic technologies and is intended for clarifying the methods available for the spectral analysis and the spectral impact on energy harvesting of the photovoltaic technologies.
Current-voltage dynamics of multi-junction CPV modules under different irradiance levels
(Elsevier Ltd, 2017-10) Eduardo F. Fernández; Juan P. Ferrer-Rodríguez; Florencia Almonacid; Pedro Pérez-Higueras
The current-voltage output of concentrator photovoltaic (CPV) modules shows a complex behaviour under irradiance variations due to the use of multi-junction solar cells and optical elements. The single exponential model (SEM) relates the I-V curve of a PV device and its five characteristic parameters. Hence, it is fundamental to understand the dependence of the SEM model and I-V parameters of CPV modules with irradiance. In this paper, two samples of concentrator modules were characterized under fully controlled conditions by using a CPV solar simulator for light intensities within 700–1000 W/m2. Results show that the photo-generated current increases linearly with irradiance, the diode ideality factor and saturation current present a stable behaviour under irradiance variations while the parasitic resistances, i.e. series and shunt, trend to decrease as the intensity increases. In addition, different approximations are carried out in the SEM model equation to explain the dependence of the performance of the concentrators with light intensity. Finally, the prediction of the I-V curves and key electrical parameters from reference values is discussed. Results show a good fitting of the I-V curves until 750 W/m2 and a high accuracy in the estimations of the electrical parameters with a MAPE lower than 1.2%, a MRE within 1% and a R2 equal of higher than 0.9.
Global energy assessment of the potential of photovoltaics for greenhouse farming
(Elsevier Ltd, 2022-03-01) Eduardo F. Fernández; Antonio Villar-Fernández; Jesús Montes-Romero; Laura Ruiz-Torres; Pedro M. Rodrigo; Antonio J. Manzaneda; Florencia Almonacid
Agrivoltaic (APV) systems have emerged as a promising solution to reduce the land-use competition between PV technology and agriculture. Despite its potential, APV is in a learning stage and it is still necessary to devote big efforts to investigate its actual potential and outdoor performance. This work is focused on the analysis of APV systems in agriculture greenhouses at global scale in terms of energy yield. To conduct this study, we introduce here a novel dual APV model, which is projected in four representative locations with a high crop cultivation greenhouse implantation, i.e. El Ejido (Spain), Pachino (Italy), Antalya (Turkey) and Vicente Guerrero (Mexico), and for 15 representative plant cultivars from 5 different important socioeconomic families of crops, i.e. Cucurbitaceae, Fabaceae, Solanacae, Poaceae, Rosaceae. At this stage, semi-transparent c-Si PV technology has been considered due its high efficiency and reliability. The results show that APV systems could have a transparency factor around 68% without significantly affecting the total crop photosynthetic rate. Taking this into account, APV systems would produce an average annual energy around 135 kWh/m2, and values around 200 kWh/m2 under a favourable scenario. This could represent a contribution to the total market share between 2.3% (México) and 6% (Turkey), and up to 100% of the consumption demand of greenhouses equipped with heating and cooling (GSHP), and lighting.
Non-invasive detection of pesticide residues in freshly harvested olives using hyperspectral imaging technology
(Elsevier, 2024-11-07) Martínez Gila, Diego M.; Bonillo Martínez, David; Satorres Martínez, Silvia; Cano Marchal, Pablo; Gámez García, Javier
Pesticides play a crucial role in boosting the overall yield and productivity of agricultural produce by controlling pests, insects, and various plant diseases. However, excessive use of pesticides has led to contamination of food products and water bodies, as well as disruption of ecological and environmental systems. Global health authorities have set limits for pesticide residues in individual food items to ensure the availability of safe foods in the supply chain and to assist farmers in developing optimal agronomic practices for crop production. In Spain, specifically regarding olive cultivation, the Ministry of Agriculture, Fisheries, and Food establishes a safety period that farmers must observe from the application of the pesticide until the fruit is harvested. This period ensures that the batch of olives will comply with the maximum residue level allowed. This article proposes a methodology based on hyperspectral imaging to detect whether the olives have been sprayed with pesticide products and, if so, when the spraying occurred. The proposed methodology operates at the pixel level, where each pixel of the hyperspectral image is an instance. The pesticides evaluated were Diflufenican, Oxyfluorfen, Deltamethrin, 𝜆- Cyhalothrin, and Tebuconazole. The results are promising and the success rates achieved over 80% accuracy for most pesticides in controlled laboratory conditions, with individual performance varying according to each pesticide’s chemical properties and stability on the olive surface. While the results are promising, the scalability of this approach for larger and more diverse batches of olives requires validation under field conditions, where variations in environmental factors, olive variety, and ripeness may impact the detection accuracy. Furthermore, the study highlights key wavelengths around 750 nm and 550 nm as effective discriminators, suggesting potential for cost-effective, simplified imaging systems. Although hyperspectral imaging shows potential as an accessible, in-line monitoring solution for cooperative use, further analysis of implementation costs is recommended to confirm its feasibility on an industrial scale.
CACESOL: Characterization of photovoltaic solar cells and modules. Experimental measurement of the V-I curves
(Journal Science IES, 2016-06-27) Amate-Marchal, M.; Castro-Valderas, J.; Bueno Rodríguez, R.; Pérez-Jiménez, F. J.; Gómez-Macías, A.; Medina-Rincón, A.; Aguilar-Peña, J. D.; Rus-Casas, C.; Muñoz-Rodríguez, F.
La obtención y translación a condiciones estándar de medida (CEM) de las curvas características V-I tomadas a sol real de una célula y módulo fotovoltaico, probablemente sea una de las prácticas imprescindibles en cualquier trabajo de investigación relacionado con la tecnología fotovoltaica. Este trabajo trata de obtener los distintos parámetros de las curvas de funcionamiento de la célula y módulo fotovoltaico, para diferentes condiciones de iluminación (irradiancia), pasar a valores en condiciones estándar de medida y comparar con las proporcionadas en las hojas de características por el fabricantede dispositivos fotovoltaicos (FV). Se demuestra la dependencia de la corriente con la irradiancia recibida sobre la célula solar. Nuestro estudio se realizó en el laboratorio (indoor) y en condiciones a sol real (outdoor) con un mini módulo de silicio monocristalino (CS20M20 de 2,36v, 1.21A, 2.2Wp), y módulo de silicio policristalino (Shell RSM 100S de 100Wp). Los parámetros a medir son: la temperatura de la célula (Tc), temperatura ambiente (Ta), irradiancia (G), la corriente en cortocircuito (ISC), tensión en circuito abierto (VOC). Para la obtención de la curva VI en interior se utilizaron distintos valores de resistencias eléctricas para la medida de la tensión en extremos del módulo y la corriente del circuito. La instrumentación utilizada para la medida de los distintos parámetros fue, termómetro de temperatura de superficie y temperatura ambiente, voltímetro DC, pinza amperimétrica y medidor de irradiancia. Una vez realizadas las distintas medidas, se han hecho los cálculos necesarios para mostrar el comportamiento de los módulos ensayados.

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