The assessment of photovoltaic devices, which convert light energy into electricity, has become significantly more relevant due to the aspiration to reduce pollution on a global scale. In this context, the pursuit of optimizing the efficiency of converting light energy into electrical energy involves exhaustive studies and structural analyses of solar cells, all directed toward achieving this goal. This study introduces a research proposal aimed at analyzing the losses associated with series resistance (Rs). The analysis takes into account each component comprising this resistance, proposing a network of resistances that precisely models each of these elements. The aforementioned research focused on simple-structured crystalline silicon cells. During this investigation, the junction depth (xj) of the n-p materials was varied, with the aim of achieving efficiencies in the range of 12%. However, in the pursuit of this desired efficiency, a significant impact on series resistance was observed when analyzing the I-V curves of the cells obtained in each manufacturing process.

The assessment of photovoltaic devices, which convert light energy into electricity, becomes significantly relevant due to the aspiration to reduce pollution on a global scale. In this context, the pursuit of optimizing the efficiency in converting light energy into electrical energy involves exhaustive studies and structural analyses of solar cells, all directed toward achieving this goal. This study introduces a research proposal aimed at analyzing the losses associated with series resistance (Rs). The analysis takes into account each component comprising this resistance, pro-posing a network of resistances that precisely models each of these elements. The aforementioned research focused on simple-structured crystalline silicon cells. During this investigation, the junction depth (xj) of n-p materials was varied with the aim of achieving efficiencies in the range of 12%. However, in the pursuit of this desired efficiency, a significant impact on series resistance was observed when analyzing the I-V curves of the cells obtained in each manufacturing process.

Accurate extraction of single-diode model (SDM) parameters is essential for photovoltaic performance analysis, especially when only datasheet values or graphical I-V characteristics are available. This study presents a graphical–deterministic parameter extraction framework that combines calibrated curve digitization, local differential analysis, uncertainty-aware slope estimation, and analytical SDM closure. Unlike conventional datasheet-based analytical methods that operate directly from tabulated characteristic points, the proposed approach explicitly incorporates the graphical-to-numerical conversion stage and evaluates its impact on the estimation of the resistive parameters. The shunt and series resistances are obtained as effective local slope estimates near short-circuit and open-circuit conditions, respectively, while the photocurrent, saturation current, and ideality factor are determined from characteristic operating point equations. The ideality factor is solved through a deterministic scalar root procedure within the physically admissible interval $1\le \mathrm{n}\le 2$. The method was evaluated using four photovoltaic devices, including laboratory-scale cells and commercial modules. Normalized reconstruction errors of 0.82% and 0.88% were obtained for the RTC-France cell and the KC200GT module, respectively. The INAOE laboratory cell and the SP450M half-cut module showed higher sensitivity to graphical slope extraction and energetic closure. For the SP450M module, the use of an equivalent series cell number ${\mathrm{N}}_{\mathrm{S},\mathrm{e}\mathrm{q}}$ = 72 improved agreement with the digitized graphical I–V curve, although the reconstructed maximum power remained below the nominal datasheet value, revealing a graphical/datasheet consistency issue. These results show that the proposed framework is a transparent and reproducible alternative for SDM identification from graphical sources, while also defining its sensitivity limits when applied to low-resolution curves or half-cut high-power modules with complex equivalent electrical configurations.

This article proposes a method that utilizes image analysis for the extraction of parameters in solar cells and panels. The method leverages the I-V curve image, provided by the manufacturer in the device's datasheet (solar cell/panel), which is processed using image editing software (Adobe Photoshop). Using Matlab, a reference system is established, enabling the calculation of key pa-rameters such as series resistance, shunt resistance, and maximum power point of the solar device. In this way, this method eliminates the need for formulating complex systems of equations, conducting prior measurements, employing costly equipment, or acquiring a dataset, as is the case with other parameter extraction methods for solar devices.