2026-05-05T09:17:25Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/839172026-04-06T19:04:31Zcom_10324_1166com_10324_931com_10324_894col_10324_1338

00925njm 22002777a 4500 dc Mateo Romero, Héctor Felipe author Morales Aragones, José Ignacio author Hernández Callejo, Luis author González Rebollo, Miguel Ángel author Cardeñoso Payo, Valentín author Alonso Gómez, Víctor author Carbonó de la Rosa, Mario Eduardo author García Mateos, Ginés author 2026 The estimation of series resistance in photovoltaic (PV) cells is a crucial parameter that significantly influences their efficiency and overall performance. This study proposes a novel methodology to predict the slope of the current–voltage (Ⅰ–Ⅴ) curve of a PV cell in the first quadrant, where this slope (the electrical conductance) is directly associated with the series resistance of the cell. By leveraging artificial intelligence techniques, a convolutional neural network model has been developed to estimate this slope from electroluminescence (EL) images of the cells. The model was trained on a dataset consisting of EL images of PV cells with artificially induced defects, together with the corresponding slope values derived from the cells' Ⅰ–Ⅴ curves. Furthermore, this work presents a second model that combines the slope information and EL images to improve the prediction of the maximum power point (MPP) of a PV cell, surpassing previous approaches that rely solely on EL imagery. Both models demonstrated low error rates across multiple evaluation metrics, evidencing their accuracy and robustness. Additionally, comparative analysis with other machine learning methods highlights the competitive performance of the proposed approaches. These contributions provide promising tools for enhancing the assessment and diagnosis of PV cell efficiency and reliability, potentially leading to improved performance and increased longevity of photovoltaic systems. Mathematical Biosciences and Engineering, 2026, vol. 23, n. 5, p. 1269-1288 1551-0018 https://uvadoc.uva.es/handle/10324/83917 10.3934/mbe.2026046 1269 5 1288 Mathematical Biosciences and Engineering 23 CNN-based estimation of series resistance in photovoltaic cells from electroluminescence images with application to output power prediction