No abstract

The long-term reliability of photovoltaic modules is crucial to ensure the technical and economic viability of PV as a successful energy source. The analysis of degradation mechanisms of PV modules is key to ensure current lifetimes exceeding 25 years. This paper presents the results of the investigations carried out on the degradation mechanisms of a crystalline silicon PV installation of 2 kWp after 12 years of exposure in Málaga, Spain. The analysis was conducted by visual inspection, infrared thermography and electrical performance evaluation. By visual inspection, the most relevant defects in the modules were identified and ranked according to their frequency. The electrical performance was assessed by comparing the characteristic parameters of the individual modules, obtained by outdoor measurements at the start and end of the exposure period. The correlation of the visual defects and the shifts in the electrical parameters was analysed. The results presented show that glass weathering, delamination at the cell-EVA interface and oxidation of the antireflective coating and the cell metallization grid were the most frequently occurring defects found. The total peak power loss, including the initial light induced degradation, was 11.5%, which corresponded almost totally to a loss in short-circuit current.

No abstract

The accumulation of dust on the surface of a photovoltaic module decreases the radiation reaching the solar cell and produces losses in the generated power. Dust not only reduces the radiation on the solar cell, but also changes the dependence on the angle of incidence of such radiation. This work presents the results of a study carried out at the University of Malaga to quantify losses caused by the accumulation of dust on the surface of photovoltaic modules. Our results show that the mean of the daily energy loss along a year caused by dust deposited on the surface of the PV module is around 4.4%. In long periods without rain, daily energy losses can be higher than 20%. In addition, the irradiance losses are not constant throughout the day and are strongly dependent on the sunlight incident angle and the ratio between diffuse and direct radiations. When studied as a function of solar time, the irradiance losses are symmetric with respect noon, where they reach the minimum value. We also propose a simple theoretical model that, taking into account the percentage of dirty surface and the diffuse/direct radiation ratio, accounts for the qualitative behavior of the irradiance losses during the day.

The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to X-ray and CT-scan medical images for the detection of COVID-19. In this paper, we used four powerful pre-trained CNN models, VGG16, DenseNet121, ResNet50,and ResNet152, for the COVID-19 CT-scan binary classification task. The proposed Fast.AI ResNet framework was designed to find out the best architecture, pre-processing, and training parameters for the models largely automatically. The accuracy and F1-score were both above 96% in the diagnosis of COVID-19 using CT-scan images. In addition, we applied transfer learning techniques to overcome the insufficient data and to improve the training time. The binary and multi-class classification of X-ray images tasks were performed by utilizing enhanced VGG16 deep transfer learning architecture. High accuracy of 99% was achieved by enhanced VGG16 in the detection of X-ray images from COVID-19 and pneumonia. The accuracy and validity of the algorithms were assessed on X-ray and CT-scan well-known public datasets. The proposed methods have better results for COVID-19 diagnosis than other related in literature. In our opinion, our work can help virologists and radiologists to make a better and faster diagnosis in the struggle against the outbreak of COVID-19.