RT info:eu-repo/semantics/article
T1 Photovoltaic Cells Defects Classification by Means of Artificial Intelligence and Electroluminescence Images
A1 Mateo Romero, Héctor Felipe
A1 Pérez-Romero, Álvaro
A1 Hernández Callejo, Luis
A1 Gallardo-Saavedra, Sara
A1 Alonso-Gómez, Víctor
A1 Morales-Aragonés, José Ignacio
A1 Plaza, Alberto Redondo
A1 Martínez, Diego Fernández
AB More than half of the total renewable addictions correspond to solar photovoltaic (PV) energy. In a context with such an important impact of this resource, being able to produce reliable and safety energy is extremely important and operation and maintenance (O&M) of PV sites must be increasingly intelligent and advanced. The use of Artificial Intelligence (AI) for the defects identification, location and classification is very interesting, as PV plants are increasing in size and quantity. Inspection techniques in PV systems are diverse, and within them, electroluminescence (EL) inspection and current-voltage (I-V) curves are one of the most important. In this sense, this work presents a classifier of defects at the PV cell level, based on AI, EL images and cell I-V curves. To achieve this, it has been necessary to develop an instrument to measure the I-V curve at the cell level, used to label each of the PV cells. In order to determine the classification of cell defects, CNNs will be used. Results obtained have been satisfactory, and improvement is expected from a greater number of samples taken.
SN 1865-0929
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/66007
UL https://uvadoc.uva.es/handle/10324/66007
LA spa
NO Mateo-Romero, H.F. et al. (2022). Photovoltaic Cells Defects Classification by Means of Artificial Intelligence and Electroluminescence Images. In: Nesmachnow, S., Hernández Callejo, L. (eds) Smart Cities. ICSC-Cities 2021. Communications in Computer and Information Science, vol 1555. Springer, Cham. https://doi.org/10.1007/978-3-030-96753-6_3
DS UVaDOC
RD 19-dic-2024