Defect detection and quantification in electroluminescence images of solar PV modules using U-net semantic segmentation

Deep learning based automatic defect identification of photovoltaic module using electroluminescence images

This review paper presents a comprehensive analysis of electroluminescence (EL) imaging techniques for photovoltaic (PV) module diagnostics, focusing on advancements from conventional indoor imaging to outdoor and daylight EL imaging. It examines key challenges, including ambient light interference and environmental variability, and highlights innovations such as infrared-sensitive indium gallium arsenide (InGaAs) cameras, optical filtering, and periodic current modulation to enhance defect detection. The review also explores the role of artificial intelligence (AI)-driven methodologies, including deep learning and generative adversarial networks (GANs), in automating defect classification and performance assessment. Additionally, the emergence of drone-based EL imaging has facilitated large-scale PV inspections with improved efficiency. By synthesizing recent advancements, this paper underscores the critical role of EL imaging in ensuring PV module reliability, optimizing performance, and supporting the long-term sustainability of solar energy systems.

Cracks in solar cells are one of the problems responsible for low efficiency among photovoltaic (PV) modules and these cracks are hard to avoid. Cracks usually can occur during transportation, handling or manufacturing process. Micro cracks degrade the power output in photovoltaic modules due to the inactive cell area. Micro-cracks are invisible to detect by naked eye and hence the Electroluminescence (EL) imaging were introduced to analyse the cracks in PV module. The electroluminescence is the most useful method to detect the cracks in the solar cell. A si-CCD camera was used in the dark condition to capture the EL image under forward bias conditions. In this study, mono-crystalline and multicrystalline were taken from the outdoor condition which were exposed for almost eight years and were analysed using electroluminescence imaging. From the electroluminescence imaging, the cracks were measured. The cracks were analysed by pattern and both panels were compared. From the study it was found that multicrystalline photovoltaic module shows the more crack compared with monocrystalline photovoltaic module. The crack in the individual solar cell and their relative efficiency in the two different types of crystalline modules have been also been presented.

Automated defect identification in electroluminescence images of solar modules

Electroluminescence (EL) imaging is one of the most common characterisation techniques for photovoltaic cells and modules. EL images contain information on both the open-circuit voltage (Voc) and series resistance (Rs) of the device. However, separating the two effects and identifying features related to each parameter can be challenging. In this study, a novel approach for decomposing EL images into Voc and Rs maps using a convolutional neural network architecture is presented. A deep learning model was first trained on paired EL and photoluminescence images that were generated using a simulation tool. Results obtained using the validation set show that the trained model is able to accurately differentiate between features related to Voc and Rs in EL images, thus replacing the need for multiple types of measurements. The proposed method presents a unique approach to analyse EL images, unlocking new capabilities that have the potential to advance solar cell characterisation.

Accurate current–voltage (IV) characterization is essential for assessing photovoltaic (PV) module performance, yet conventional IV tracing requires physical contact and controlled conditions, limiting large-scale deployment. Electroluminescence (EL) imaging, while highly effective for detecting localized defects, remains largely qualitative and indirect in estimating actual PV module power loss. This study introduces a deep learning framework that directly predicts complete IV curves from EL images, transforming EL inspection into a quantitative, non-contact diagnostic tool. In this work, we propose a convolutional neural network (CNN) that learns the nonlinear mapping between paired EL images captured at 20% and 80% of the short-circuit current and the corresponding IV response. A total of 438 PV modules were used for model development, with performance evaluated on unseen data. The trained CNN reconstructs IV curves with high fidelity, achieving a validation accuracy of approximately 95% and low parameter deviations (<2% for key metrics such as maximum power point and fill factor). The model maintains consistent accuracy even when a single EL image is provided, supporting flexible field operation. Inference is rapid, requiring less than 0.5 s per PV module inspection, enabling real-time analysis.

Electroluminescence (EL) imaging is a photovoltaic (PV) module characterization technique, which provides high accuracy in detecting defects and faults, such as cracks, broken cells interconnections, shunts, among many others; furthermore, the EL technique is used extensively due to a high level of detail and direct relationship to injected carrier density. However, this technique is commonly practiced only indoors—or outdoors from dusk to dawn—because the crystalline silicon luminescence signal is several orders of magnitude lower than sunlight. This limits the potential of such a powerful technique to be used in utility scale inspections, and therefore, the interest in the development of electrical biasing tools to make outdoor EL imaging truly fast and efficient. With the focus of quickly acquiring EL images in daylight, we present in this article a drone-based system capable of acquiring EL images at a frame rate of 120 frames per second. In a single second during high irradiance conditions, this system can capture enough EL and background image pairs to create an EL PV module image that has sufficient diagnostic information to identify faults associated with power loss. The final EL images shown in this work reached representative quality SNRAVG of 4.6, obtained with algorithms developed in previous works. These drone-based EL images were acquired with global horizontal solar irradiance close to one sun in the plane of the array.

Efficient deep feature extraction and classification for identifying defective photovoltaic module cells in Electroluminescence images

Inequalities in photovoltaics modules reliability: From packaging to PV installation site

The aim of this paper is to determine whether photovoltaic (PV) cells can be automatically identified as either defective or normal from electroluminescence (EL) images. This paper utilizes an experimental methodology to address the identified research problem. This paper provides evidence that deep learning (DL) can be used to distinguish between a defective and a normal PV cell. The results of this research confirm that techniques from the Computer Science discipline can be applied in photovoltaics to alleviate the tedious processes used in identifying defective PV cells from EL images.

Electroluminescence (EL) imaging of Si-based photovoltaic (PV) modules is used widely to spatially detect and characterize electrical defects, including handling and degradation-induced cracking of the component Si cells that are associated with reductions in module performance. In the present study, a commercial polycrystalline silicon PV module was subjected to accelerated lifecycle test environmental conditions and examined as a function of environmental exposure time using EL imaging. The approach followed pixel intensity distributions over each individual PV cell and confirmed a positive correlation between module conversion efficiency and results of the image analysis. Overall, an average of a 2.5% reduction in normalized EL intensity was correlated to a 0.35% reduction in actual power conversion efficiency (or a 2.3% decrease in relative efficiency). The imaging analysis technique offers a rapid, unsupervised means to assess EL data in lieu of conventional visual interpretation.

A unique non-destructive characterization method for apparent bandgap imaging in photovoltaic (PV) devices based on acquisition of two electroluminescence (EL) images in different spectral ranges is presented. The method consists of a calibration procedure and a bandgap imaging procedure. Calibration has to be performed once per module type and EL imaging setup, and must provide a relation between the bandgap and the ratio between two spectrally independent EL images. After calibration, bandgap imaging only requires acquisition of two spectrally independent EL images followed by image processing, making the method very fast and suitable for in-line PV module characterization with regard to spatial (in)homogeneity and production process stability. The method is demonstrated on a commercial state-of-the-art Cu(In,Ga)Se2 PV module where apparent bandgap fluctuations between 1.07 and 1.15 eV are detected. Copyright © 2016 John Wiley & Sons, Ltd.

Experimental assessment of long-term performance degradation for a PV power plant operating in a desert maritime climate

We present a non-destructive measurement technique to determine the operating of individual solar cells in industrial manufactured photovoltaic (PV) module. This technique is based on electroluminescence imaging and makes use of two physical observations. First, the highest electroluminescence signal of each solar cell is proportional to its operating and second, the sum of all operating voltages equals the externally applied PV voltage. Thus, the measurement of the peak signals in the electroluminescence image for each cell provides the operating cell voltages. The reliability of this relation is verified by applying the method to a specially prepared PV where we measure the individual operating cell voltages directly on exposed interconnectors. The cell voltages experimentally measured by EL are determined with a high accuracy of ± 0.6%. Moreover, the operating cell voltages determined from the electroluminescence image are used to calculate the PV series resistance. We call this method voltage imaging of the PV module (VIM). Furthermore we present image pre-processing methods to reduce the impact of a number of artificial effects to our VIM approach. Keywords: PV module, electroluminescence

Photovoltaic (PV) modules' degradation behaviour, together with outdoor field condition and fault diagnostics, consist valuable data in evaluating efficiency and establishing long-term reliability of a PV system. However, since PV modules commonly come with 25 (even over) years of warranty, testing modules in field for that long period is not feasible. Thus, due to time constraints, many accelerated aging tests have been recently introduced for testing reliability and durability of PV modules. Scope of the presented study is the assessment of the performance degradation and fault (defect) propagation mechanisms with regard to three monocrystalline silicon (mc-Si) PV modules, through a specific thermal cycle accelerated ageing test. The parameters that were experimentally evaluated towards the accelerated ageing process of this study, were: i) the thermal signature evolution and/or propagation of any diagnosed defects, especially in two modules, by means of infrared (IR) thermographic measurements, ii) the overall performance degradation of each module, by means of electrical I-V measurements, together with iii) specific morphological characteristics, by means microscopy imaging. The intended study gave useful and promising results in assessing the effect of the applied thermal cycles upon the modules’ thermal behaviour, electrical efficiency and morphology.