http://hdl.handle.net/10174/37664 2026-05-08T02:16:51Z http://hdl.handle.net/10174/41963 Title: Impact of Measurement Noise and Fitting Window Placement on Single-Diode PV Parameter Extraction Authors: Mesbahi, Oumaima; Afonso, Daruez; Janeiro, Fernando M; Grilo, Frederico; Tlemçani, Mouhaydine Abstract: The problem of photovoltaic (PV) cell degradation can affect the shape of the I-V curve, which can lead to variations in the five parameters of the PV cell. This is the motivation behind the importance of knowing and extracting these parameters. The process starts by the measuring the output current and voltage (I-V curve) then applying a best fit to obtain the parameters. Both the noise of the instruments used for measurement and the size of the measured window can affect the accuracy of the obtained parameters. This paper presents a study about the effects of both the noise of instruments and the interval size. Varying the RMS of the noise of both current and voltage from 1 to 10%, the parameters are extracted from two case studies, first one starting the interval from the short circuit coordinates and the second one from the open circuit voltage, the size of the intervals are increased till reaching the whole curve. Results demonstrated that to obtain optimized parameters a 40−60% segment of the I-V curve should be measured staring from Voc region. 2025-10-21T23:00:00Z http://hdl.handle.net/10174/41962 Title: Automated Detection of Aircraft Surface Defects Using Deep Learning with Integrated Human Validation Authors: Mesbahi, Oumaima; Chabane, Souhila; Pereira Santos, Nuno; Del Pino Lino, Adriano; Tlemçani, Mouhaydine; Lourenço Da Saúde, José Manuel Abstract: Visual inspection of aircraft surface is one of the many steps in the maintenance routines. Usually performed by operators, this procedure might last days to be accomplished. The use of automated process can help reduce time and results in accurate detection of surface defects on aircraft, as they are vital to maintain structural soundness and flight safety. This paper proposes a deep learning framework for automated defect detection based on Faster R-CNN with ResNet-50 Feature Pyramid Network (FPN) as the backbone model. This model was trained and validated on a sizable, labeled aircraft images with a maximum F1-score of 0.555 achieved in the test set. This is the result of preliminary study, where the authors aimed to detect all types of defects without classification. To further enhance reliability and allow for human input, a custom annotation validation user interface was implemented via Python, which allowed aircraft inspectors to view, edit, add, and acknowledge predictions made by the model in an attempt to hold onto precise level of annotation. This system also facilitated the management of annotations, visualization on irregular aircraft zones, and the creation of reports thus allowing for inspection workflows. The results show that combining state-of-the-art object detection with domain expertise in validation as route to reliable semi-automatic, standards-compliant aircraft defect detection is plausible. Future work will involve expanding the dataset, tuning for accuracy, and incorporating human feedback for enhancement of model utility over time. 2025-10-21T23:00:00Z http://hdl.handle.net/10174/41960 Title: Dry Transformer Specification for Photovoltaic Power Plants: Investigation Into the K-Factor and the Use of 800VAC Disconnectors Authors: Esposito, Marcelo; Prestes Kunz, Matheus; Mesquita Bruel, Gabriela; Belén Cristóbal López, Ana; Luis André Pereira Fialho, Luis; Mesbahi, Oumaima Abstract: Although there are several transformer manufacturers in Brazil dedicated to photovoltaic power generation, the connection between inverters with a nominal output voltage of 800VAC and the protection system installed before the step-up transformer is still complex. In this paper, issues related to design, installation and supply of equipment and preventive and corrective maintenance are explored. Operating data is presented for two photovoltaic plants (PV), one has a power of 444kWp and the other 406kWp, which share the same installation design. Both plants experienced faults including, in the worst case, a short circuit followed by a fire caused by the general disconnector. The transformers of 500kVA and K1 factor were assessed after the accidents and showed no faults in the measurements taken at the substations. The installation of disconnect switches with a nominal operating voltage (Ue) equal to 690VAC was identified as the cause of the short circuit. The quality of the equipment was investigated and an analysis of the operating temperature of the transformers was carried out. Possible causes and effects, such as the presence of harmonics and inverter failures, were explored. Given the high cost of circuit breakers for 800VAC/400A Ue and transformers with a K4 factor, the solution found was to repair them and replace the faulty cables and switch-disconnectors with products dedicated to photovoltaic systems. The use of fuses in the AC circuit made the disconnection difficult and impaired the safety in several aspects. 2025-01-01T00:00:00Z http://hdl.handle.net/10174/41952 Title: ANÁLISE EXPLORATÓRIA DO USO DA MODELAGEM ATMOSFÉRICA SOBRE OS INCÊNDIOS FLORESTAIS NO PANTANAL Authors: Couto, Flavio Tiago; Santos, Filippe L. M.; Campos, Cátia; Purificação, Carolina; Andrade, Nuno; López-Vega, Juan Manuel; Lacroix, Matthieu Abstract: O estudo discute de forma exploratória as condições atmosféricas favoráveis à evolução dos incêndios florestais no Pantanal em 12 de novembro de 2023. Esses episódios foram marcados por dois períodos de rápida expansão do fogo, primeiro no início da tarde e outro à noite. O estudo usa um conjunto de observações de satélite e estações meteorológicas, as quais ajudaram a identificar o fogo e algumas condições meteorológicas na superfície. Além disso, o Fire Weather Index (FWI) no Pantanal foi analisado para um período de 44 anos. No entanto, esse conjunto de dados não foi suficiente para explicar completamente o comportamento do fogo naquele dia. Nesse contexto, a modelagem atmosférica foi aplicada para encontrar as possíveis causas do comportamento do fogo em dois períodos. O modelo Meso-NH foi configurado com dois domínios aninhados e resoluções horizontais de 2500 m e 500 m. Os resultados mostraram uma tendência positiva do FWI nas últimas décadas, bem como uma clara sazonalidade para os valores máximos no ano de 2023. A simulação indicou condições favoráveis à ignição do fogo, e o campo de rajadas de vento mostrou ventos moderados em ambos os períodos, mas causados por diferentes forçantes. No início da tarde, a circulação em grande escala favoreceu a propagação do fogo, enquanto à noite uma frente de rajada foi observada. O estudo destaca o papel das condições meteorológicas na escala sub-diária, em particular para mudanças repentinas do vento à superfície ao longo do dia. Esse resultado deve ser considerado ao examinar o perigo de fogo e o planejamento das ações de combate aos incêndios na região. 2025-08-19T23:00:00Z