Micro-Fracture Detection in Photovoltaic Cells with Hardware-Constrained
  Devices and Computer Vision
Booy Vitas Faassen Jorge Serrano Paul D. Rosero-Montalvo
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.