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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Accelerated Stress Testing of Perovskite Photovoltaic Modules: Differentiating Degradation Modes with Electroluminescence Imaging

Herein, electroluminescence (EL) and thermal imaging are used to examine p–i–n metal halide perovskite (MHP) photovoltaic (PV) mini-modules (MA0.6FA0.4PbI3, 20 cells, 78 cm2) before and after indoor-accelerated stress testing or outdoor deployment. Distinct spatial patterns in the EL images emerge, which depend on the external stress conditions experienced by the mini-module. Imaging results highlight a distribution of dark speckle features that dominate after UV stress, attributed to widespread interfacial contact degradation. Lateral intensity gradients across cells dominate after thermal cycling (TC) stress, attributed to current crowding near scribe defects. While current–voltage analysis alone does not give full insight on the degradation process, this study shows that distinct degradation modes can be further defined by multimodal electro-optical imaging (i.e., EL combined with photoluminescence and dark lock-in thermography). Neither UV exposure nor TC-accelerated stress testing alone replicates the same degradation signatures observed after outdoor deployment, suggesting that multiple degradation modes occur under concurrent stressors outdoors. Finally, spatial characterization of degradation modes in MHP PV mini-modules before and after accelerated stress testing lays the groundwork for developing targeted accelerated stress testing procedures through comparison with outdoor aging.

14 SOLAR ENERGY↗
DOE OSTI2025

Advanced Photovoltaic Module Characterization: Using Image Transformers for Current–Voltage Curve Prediction From Electroluminescence Images

Individual photovoltaic (PV) module health monitoring can be a daunting task for operation and maintenance of solar farms. Modules can be inspected through luminescence, thermal imaging, and current–voltage (I–V) curve analyzes for identification of damage and power loss. I–V curves provide easily interpretable data to determine module health as they directly provide electrical performance metrics. However, in order to obtain these curves, modules must be disconnected from the array and either removed to a solar simulator or characterized in situ with corrections for module temperature, the incident solar spectrum, and intensity. Luminescence or thermal images of a module are relatively easy to acquire in situ. Electroluminescence (EL) images highlight physical defects in the modules but do not provide easily interpretable features to correlate with electrical performance. This work presents a SWin transformer network to predict I–V curves for PV modules from their corresponding EL images. The predicted I–V curves allow the accurate prediction of the maximum power point (MPP), short-circuit current I sc , and open-circuit voltage V oc with a mean error less of than 1%. Comparing single diode model (SDM) parameters extracted from the predicted curves to those extracted from the true curves, the series resistance R s demonstrates a mean error of 5.19%, and the photocurrent I a mean error of 0.197%. The shunt resistance R sh and dark current Io parameters are predicted with larger errors because of their sensitivity to small changes in the I–V curve.

Byford, Brandon K. [New Mexico State Univ., Las Cr↗
NASA NTRSDate not supplied

Electroluminescence Imaging: A Study in the Impact of Microscopic Surface Defects

Electroluminescence (EL) imaging can be used as a quantitative characterization method for solar cell performance when combined with image processing, allowing for the impact of dust grain size on electroluminescence imaging characteristics to be investigated. Simulating these results via modelling can help to predict what dust grain sizes, if any, will have a greater impact on performance.

photovoltaics↗
NASA NTRSDate not supplied

Electroluminescence Imaging: A Quantitative Characterization Technique to Measure Dust Occlusion of Solar Cells

Electroluminescence (EL) imaging is a qualitative characterization technique that is typically used to identify cracks, corrosion, and other defects in solar cells. It consists of imaging a cell under forward bias, where the solar cell emits photons due to radiative electron-hole pair recombination. Over the past few years, our team has expanded this into a quantitative technique with the help of image processing. We have primarily used this method to investigate lunar dust occlusion of solar cells and arrays. Lunar dust accumulation on solar cells is a major concern because it directly limits light accessible to the cell, decreasing power output. Many teams are working to develop dust mitigation technology to protect these arrays on the surface of the Moon, but thoroughly characterizing their efficacy is important to ensure their success prior to launch. Here, we present EL as a quantitative characterization technique to observe dust coverage on solar cells that, when coupled with IV performance measurements, can offer unique insights into how dust coverage impacts power output. Quantitative electroluminescence imaging works by running EL images through an image processing script that first grayscales the image then plots a histogram of the brightness of each pixel. On its own, it does not offer much insight into a solar cell’s performance. However, when comparing images to a baseline pristine, undamaged, or uncoated solar cell, it can quickly provide information about the impacts of surface contaminants or damage to the cell. Here, we present a case study where quantitative EL is used to measure the efficacy of dust mitigation technology for flexible solar arrays and discuss the lessons learned about dust mitigation, testing with lunar simulant, and this characterization technique.

photovoltaics↗
NASA NTRSDate not supplied

Evaluating Electroluminescence Imaging and Image Processing as a Quantitative Solar Cell Characterization Method

Mitigating dust accumulation on the surface of solar arrays is crucial for maintaining maximum power output. We propose investigating electroluminescence imaging paired with image processing as a means of evaluating various dust mitigation techniques. Image processing was able to clearly differentiate between pristine and dusted solar cells. Paired with traditional analysis techniques, this method proves to be a quick and powerful characterization tool.

Photovoltaic↗
DOE OSTI2022

Cell dark current–voltage from non-calibrated module electroluminescence image analysis

Here, we present a fast, accurate, and reliable method of obtaining cell dark current–voltage (I–V) curves from module electroluminescence (EL) images without requiring calibration or correction. For a pristine module, EL-derived dark I–V are compared to directly probed data for a variety of changing imaging parameters: camera sensor, lens, filter, aperture width, exposure time (level of sensor saturation), number of images used, and various combinations of these. Pristine modules and those experiencing different modes and degrees of degradation are examined. A recent study of modules using five different cell technologies demonstrates the practicality of our “EL sweep” technique for performance and degradation studies.

14 SOLAR ENERGY↗
DOE OSTI2022

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

14 SOLAR ENERGY↗
DOE OSTI2023

Automatic Crack Segmentation and Feature Extraction in Electroluminescence Images of Solar Modules

The effect of cracks in solar cells on the long-term degradation of photovoltaic (PV) modules remains to be determined. To investigate this effect in future studies, it is necessary to quantitatively describe the crack features (e.g., length) and correlate them with module power loss. Electroluminescence (EL) imaging is a common technique for identifying cracks. However, it is currently challenging and time-consuming to identify cracks in a large number of EL images and quantify complex crack features by human inspection. This article introduces a fast semantic segmentation method (~0.18 s/cell) to automatically segment cracks from EL images and algorithms to extract crack features. Here we fine-tuned a UNet neural network model using pretrained VGG16 as the encoder and obtained an average F1 score of 0.875 and an intersection over union score of 0.782 on the testing set. With cracks and busbars segmented, we developed algorithms for extracting crack features, including the crack-isolated area, the brightness inside the isolated area, and the crack length. We also developed an automatic preprocessing tool for cropping individual cell images from EL images of PV modules (~0.72 s/module). Our codes are published as open-source an software, and our annotated dataset composed of various types of cells is published as a benchmark for crack segmentation in EL images.

14 SOLAR ENERGY↗
DOE OSTI2022

Extended Accelerated Stress Testing (EAST) of Glass/Glass, Glass/Backsheet and Glass/Transparent Backsheet PV Modules: Influence of EVA and POE Encapsulants: Preprint

This paper presents the indoor extended accelerated stress testing (EAST) results of glass/glass (GG), glass/backsheet (GB) and glass/transparent backsheet (GT) modules having identical cells and two different encapsulant types, ethyl-vinyl-acetate (EVA) and polyolefin-elastomer (POE). Six 4-cell modules having the above-mentioned construction combinations were subjected to extended ultraviolet (UV; 600 kWh/m2), damp-heat (DH; 2000 hours) and thermal-cycling (TC; 600 cycles) tests. The post-stress UV fluorescent imaging, electroluminescent imaging, reflectance spectrophotometry and colorimetry results indicated that the grid finger degradation and encapsulant browning are slightly higher in the GG modules compared to the GB modules. The post-stress IV test results indicated, in general, that the GG/EVA modules tend to perform inferior to the GG/POE modules with the EAST evaluation.

EVA↗
DOE OSTI2023

A Generative Model for Synthetic Electroluminescence Images

This work will develop modular, open-source model and analysis components including crack detection workflow and parameterization for quantitative inspection of large EL large datasets. These tools will allow users to quickly and accurately assess the extent and types of cracking in their modules. Measured statistical distributions of crack parameters, together with the imposed stress and electrical properties will be used to generate models to predict future crack behavior and power loss.

Pierce, Benjamin Garrett↗
DOE OSTI2024

pvcracks

SAND2024-00922O This software uses electroluminescence images to predict power loss due to cell cracks in photovoltaic modules. The software will incorporate trained variational autoencoder(s) to parameterize cell cracks detected in electroluminescence images of photovoltaic modules and relate cracks to electrical properties; reduced-order models of finite element simulations of electrical behavior of photovoltaic modules under thermomechanical stresses; reduced-order models of stress distributions; inside photovoltaic modules resulting from x-ray topography experiments; and image segmentation and splining methods to analyze x-ray topography measurements of cracked photovoltaic cells. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Hartley, James↗
DOE OSTI2025

Novel Solar Panel Defect Detection Hardware and Defect Analysis Software (CRADA Final Report)

The CRADA work involved 1) the development of a machine learning software architecture to automatically detect defects within Electroluminescence images of solar panels, and 2) demonstrations of the usefulness of UV Fluorescence (UVF) defect detection for a wide variety of defect types across many different solar panel technologies with different field or environmental chamber histories.

14 SOLAR ENERGY↗
DOE OSTI2023

Degradation of Monocrystalline Silicon Photovoltaic Modules From a 10-Year-Old Rooftop System in Florida

A system of 180 monocrystalline aluminum back-surface field modules were installed in Cocoa, Florida, for 10 years. In total, 156 modules are characterized and compared to 3 controls. Power degradation rates vary between – 0.14% to – 3.22% per year, with median and average rates of –0.92% and –1.05% per year, respectively. The losses are primarily resistive with minor optical and recombination loss contributions. Electroluminescence imaging shows a characteristic pattern, which is shown to be resistive in nature when compared to photoluminescence. Resistive losses are due to corrosion of the rear contact Ag/solder interface and, to a much lesser degree, gridline Ag oxidation. Moisture ingress through the backsheet is likely responsible for mediating corrosion. Optical losses are due mostly to a combination of antireflection coating degradation, minor encapsulant browning, and delamination. Minor front contact corrosion may contribute to recombination. Furthermore, this study expands upon previous work on this vintage of the module by examining a large sample set, comprehensive characterization including techniques not previously used on these modules, and a comparison between two other systems of different climates.

14 SOLAR ENERGY↗
DOE OSTI2022

Electroluminescence Analysis and Grading of Hail Damaged Solar Panels

We analyzed more than 4000 electroluminescence images of hail damaged solar panels from a cluster of houses in Texas. We enhanced the images for ease of analysis and classified the defects within each solar cell into categories of glass breakage, installer damage, inactive substrings, crack severity, interconnect wire problems, and whether the damage was likely caused by hail. From these statistics, we quantified each panel into five levels of hail damage for insurance claims, and four levels of overall quality for potential resale pricing. Here, we share here some statistics regarding the defects with the hope that the data is useful to others attempting to predict the invisible damage to systems based just on the easily observable glass breakage statistics.

14 SOLAR ENERGY↗
DOE OSTI2026

Evidence of Polarization‐Type Potential‐Induced Degradation (PID‐p) in the Field and Investigation of the Recovery Mechanism on Bifacial p ‐PERC Modules

This study investigates the polarization-type potential-induced degradation (PID-p) of bifacial glass/glass p-type passivated emitter rear contact (p-PERC) modules in the field and their recovery behavior. Modules were installed with three mounting configurations providing different albedo conditions. System voltage (–600, –1500, and +1500 V) was applied to the cell circuits, with respect to the grounded module frames. No degradation was observed for positively biased modules, but PID-p was identified on the rear side when cells are negatively biased, with maximum power dropping during the first days and stabilizing at values up to 8% loss. Electroluminescence images revealed a variation of the cells' PID-p susceptibility within a module. Three parameters were shown to impact the degradation rate: rear albedo light, voltage, and wetness conditions. Degraded modules were recovered either by (1) a positive bias (+1500 V), (2) outdoor illumination with the front side facing sun, (3) outdoor illumination with the rear side facing sun, or (4) dark storage. A recovery pattern was identified with I–V parameters decreasing to a local minimum before increasing to full recovery. The proposed mechanism is based on the band bending at the rear p-type Si/AlO x/SiN x interface, going from inversion to depletion and accumulation states. Full recovery was achieved in 2–7 h for the modules recovered with the rear side facing sun, four to eight nights for the modules positively biased at night, and 10–20 days for the modules with the front side facing sun. Dark storage showed slower recovery rates as I–V parameters were not improving even after 1 month. Here, the recovery rates were correlated with the net Coulombs transferred during the preceding PID stress: When more Coulombs are transferred during the degradation, the extent of degradation is greater, leading to slower recovery rates.

14 SOLAR ENERGY↗
DOE OSTI2025

Characterization of Field-Exposed Photovoltaic Modules Featuring Signs of Contact Degradation

Here, this work investigates several photovoltaic (PV) modules that have shown signs of metal contact corrosion due to field exposure in a hot and humid climate. This includes two multicrystalline silicon aluminum back surface field systems with 10 and 14 years of exposure and one monocrystalline silicon passivated emitter and rear cell system with four years of exposure. A comprehensive, multiscale characterization process is used to evaluate these PV modules in great detail. Current–voltage (I−V), Suns-V OC measurements, electroluminescence imaging, infrared imaging, and ultraviolet fluorescence imaging were performed, and locations of interest were cored and analyzed using cross-sectional scanning electron microscopy (SEM). A rigorous, quantitative analysis procedure for the cross-sectional SEM images is proposed and implemented. Careful characterization does reveal that some of these PV modules do indeed exhibit the same classic signs of acetic-acid-based corrosion of the glass frit that is present at the silver/silicon interface, which have been observed previously in PV modules exposed to damp heat in an environmental chamber.

14 SOLAR ENERGY↗