RT info:eu-repo/semantics/article
T1 Evaluation of Artificial Intelligence-Based Models for Classifying Defective Photovoltaic Cells
A1 Pérez-Romero, Álvaro
A1 Mateo-Romero, Héctor Felipe
A1 Gallardo-Saavedra, Sara
A1 Alonso-Gómez, Víctor
A1 Alonso-García, María del Carmen
A1 Hernández Callejo, Luis
AB Solar Photovoltaic (PV) energy has experienced an important growth and prospect during the last decade due to the constant development of the technology and its high reliability, together with a drastic reduction in costs. This fact has favored both its large-scale implementation and small-scale Distributed Generation (DG). PV systems integrated into local distribution systems are considered to be one of the keys to a sustainable future built environment in Smart Cities (SC). Advanced Operation and Maintenance (O&M) of solar PV plants is necessary. Powerful and accurate data are usually obtained on-site by means of current-voltage (I-V) curves or electroluminescence (EL) images, with new equipment and methodologies recently proposed. In this work, authors present a comparison between five AI-based models to classify PV solar cells according to their state, using EL images at the PV solar cell level, while the cell I-V curves are used in the training phase to be able to classify the cells based on its production efficiency. This automatic classification of defective cells enormously facilitates the identification of defects for PV plant operators, decreasing the human labor and optimizing the defect location. In addition, this work presents a methodology for the selection of important variables for the training of a defective cell classifier.
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/65994
UL https://uvadoc.uva.es/handle/10324/65994
LA spa
NO Pérez-Romero, Á.; Mateo-Romero, H.F.; Gallardo-Saavedra, S.; Alonso-Gómez, V.; Alonso-García, M.d.C.; Hernández-Callejo, L. Evaluation of Artificial Intelligence-Based Models for Classifying Defective Photovoltaic Cells. Appl. Sci. 2021, 11, 4226. https://doi.org/10.3390/app11094226
DS UVaDOC
RD 11-jul-2024