2026-05-05T13:30:23Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/660072025-01-31T08:03:22Zcom_10324_1158com_10324_931com_10324_894col_10324_1242

00925njm 22002777a 4500 dc Mateo Romero, Héctor Felipe author Pérez Romero, Álvaro author Hernández Callejo, Luis author Gallardo Saavedra, Sara author Alonso Gómez, Víctor author Morales Aragones, José Ignacio author Plaza, Alberto Redondo author Fernández Martínez, Diego author 2022 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. 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 1865-0929 https://uvadoc.uva.es/handle/10324/66007 10.1007/978-3-030-96753-6_3 31 41 1555 1865-0937 Photovoltaic Cells Defects Classification by Means of Artificial Intelligence and Electroluminescence Images