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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 OSTI2020

Generalized and Mechanistic PV Module Performance Prediction From Computer Vision and Machine Learning on Electroluminescence Images

Electroluminescence (EL) imaging of photovoltiac (PV) modules offers high-speed, high-resolution information about device performance, affording opportunities for greater insight and efficiency in module characterization across manufacturing, research and development, and power plant operations and management. Predicting module electrical properties from EL image features is a critical step toward these applications. In this article, we demonstrate quantification of both generalized and performance mechanism-specific EL image features, using pixel intensity-based and machine learning classification algorithms. From EL image features, we build predictive models for PV module power and series resistance, using time-series current-voltage (I-V) and EL data obtained stepwise on five brands of modules spanning three Si cell types through two accelerated exposures: damp heat (DH) (85°C/85% RH) and thermal cycling (TC) (IEC 61215). Overall, 195 pairs of EL images and I-V characteristics were analyzed, yielding 11700 individual PV cell images. A convolutional neural network was built to classify cells by the severity of busbar corrosion with high accuracy (95%). Generalized power predictive models estimated the maximum power of PV modules from EL images with high confidence and an adjusted-R 2 of 0.88, across all module brands and cell types in extended DH and TC exposures. Mechanistic degradation prediction was demonstrated by quantification of busbar corrosion in EL images of three module brands in DH, and subsequent modeling of series resistance using these mechanism-specific EL image features. For modules exhibiting busbar corrosion, we demonstrated series resistance predictive models with adjusted-R 2 of up to 0.73.

14 SOLAR ENERGY↗
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 OSTI2020

Methods for In Situ Electroluminescence Imaging of Photovoltaic Modules Under Varying Environmental Conditions

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.

14 SOLAR ENERGY↗
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 OSTI2021

Linking Transient Voltage to Spatially-Resolved Luminescence Imaging to Understand Reliability of Perovskite Photovoltaics

In this work, we present a methodology to separate effects of perovskite device metastability from irreversible degradation, using stress/rest cycling under constant current bias while collecting a series of electroluminescence images and continuously monitoring voltage. We develop a simulation model and procedures for image processing to better understand the effects of ion parameters on the transient nature of voltage and evolving electroluminescence images.

electroluminescence↗
DOE OSTI2021

Linking Transient Voltage to Spatially-Resolved Luminescence Imaging to Understand Reliability of Perovskite Photovoltaics: Preprint

In this work, we present a methodology to separate effects of perovskite device metastability from irreversible degradation, using stress/rest cycling under constant current bias while collecting a series of electroluminescence images and continuously monitoring voltage. We develop a simulation model and procedures for image processing to better understand the effects of ion parameters on the transient nature of voltage and evolving electroluminescence images.

41 EE - Solar Energy Technologies Office (EE-4S)↗