Sistema de clasificación de fallas en tiempo real de paneles fotovoltaicos mediante uso de imágenes RGB y aprendizaje profundo
This study presents a proposal for a classification system for faults that occur in photovoltaic modules from the use of RGB images and a deep learning approach implemented in a portable embedded device (Raspberry Pi). Convolutional Neural Networks (CNN) are used for photovoltaic module detection us...
- Autores:
-
Flórez Sierra, Andrés Felipe
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2020
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/51548
- Acceso en línea:
- http://hdl.handle.net/1992/51548
- Palabra clave:
- Paneles solares fotovoltáicos
Redes neuronales convolucionales
Fallas de sistemas (Ingeniería)
Energía solar
Ingeniería
- Rights
- openAccess
- License
- https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
Summary: | This study presents a proposal for a classification system for faults that occur in photovoltaic modules from the use of RGB images and a deep learning approach implemented in a portable embedded device (Raspberry Pi). Convolutional Neural Networks (CNN) are used for photovoltaic module detection using semantic segmentation and additionally for fault classification. This study considers 2 specific cases of classification; Binary (Fault and not Fault) and quaternary (Cracks, Shadows, Dust and Not Fault). The models presented show an average accuracy greater than 90% for the binary classification and 74% for the quaternary classification. The implementation of these models in low cost embedded devices corresponds to a viable alternative for the continuous monitoring of photovoltaic modules. |
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