RT info:eu-repo/semantics/article
T1 Micro-Fracture Detection in Photovoltaic Cells with Hardware-Constrained Devices and Computer Vision
A1 Serrano Gutierrez, Jorge
A1 Faasen, Booy Vitas
A1 Rosero-Montalvo, Paúl D.
AB Solar 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.
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/66613
UL https://uvadoc.uva.es/handle/10324/66613
LA eng
NO arXiv:2403.05694
DS UVaDOC
RD 17-may-2024