Comparison of electroluminescence image capture methods

Automated defect identification in electroluminescence images of solar modules

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

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.

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.

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

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.

Electroluminescence (EL) imaging and infrared (IRT) thermography techniques have become indispensable tools in recent years for health diagnostic of photovoltaic modules in solar industry application. We propose a diagnostic setup, which performs lock-in EL for accurate analysis of different types of faults occurring in a solar module. The setup is built around a high-speed SWIR camera, which can acquire images at very short integration time (1µs) and high frame rate (301 fps). In addition, a state-of-the-art imaging chamber allows for introducing controlled levels of ambient light noise for developing new light noise removal methods, rotation of panel frame in 3 axes plane for developing perspective distortion correction techniques. The paper also gives an insight of different system and communication delays that affects the performance of overall EL lock-in imaging system integration. The purpose of the diagnostic setup is to support research in PV failure quantification through EL imaging, which can also be useful for aerial drone imaging of PV plants.

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.

Electroluminescence (EL) imaging is a powerful tool used to identify defects in photovoltaic solar cells. Typically, this type of characterization is performed in the dark using a current injection that equals short-circuit current measured at standard test conditions (STC). Restricting imaging to such a temperature range limits the information obtained about the module and cells. However, it is not trivial to develop a tool that would allow for EL imaging to be performed under a wider range of temperatures. Here we demonstrate an in situ EL imaging capability developed within an environmental chamber that allows for control of sample temperatures between –40 and 90 °C. Additionally, we demonstrate EL imaging of 4-cell mini-modules (MiMo) under front-side mechanical loading. A Raspberry Pi-connected camera with short-pass filter removed is used for EL imaging. The camera is low-cost with a small form-factor, appropriate for use in a harsh, enclosed environment. The camera is installed within a thermally isolating housing mounted within the environmental chamber. Three example cases are given for MiMos that exhibit various forms of degradation including solder fatigue and cell cracking. It is shown that by measuring at conditions above and below STC, different behaviors may be identified. In some cases, restricting characterization to STC may lead to a failure to detect damage existing in the sample.

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.

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

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.

Thermal and electroluminescence (EL) imaging techniques are widely accepted as powerful tools for analyzing solar cells. We have identified and characterized various defects in photovoltaic devices with sub-micron spatial resolution using a novel thermoreflectance imaging technique that can simultaneously obtain thermal and EL images with a mega-pixel silicon-based CCD. Linear and non-linear shunt defects are investigated as well as electroluminescent breakdown regions at reverse biases as low as -5V. Pre-breakdown sites with electroluminescence are observed. The wavelength flexibility of thermoreflectance imaging is explored and thermal images of sub-micrometer defects are obtained through glass that would typically be opaque for infrared light. Image sequences show a 10μs thermal transient response of a 15μm defect in a polysilicon solar cell. Nanosecond reverse bias voltage pulses are used to detect breakdown regions in thin-film a-Si solar cells with EL.

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.