{"title":"Approaches to Mitigate Edge Recombination Effects in Silicon Lifetime Samples With Emitter","authors":"David Bäurle;Axel Herguth;Giso Hahn","doi":"10.1109/JPHOTOV.2025.3568471","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3568471","url":null,"abstract":"Insufficiently sized symmetric lifetime samples with pn-junction exhibit a specific injection-dependent effective charge carrier lifetime measured by photoconductance decay due to increased edge recombination, characterized by a strong decline toward low injection. In this study, various approaches are presented to suppress these edge effects in n-type Si samples with boron emitter. These approaches include edge passivation using AlO<inline-formula><tex-math>$_{text{x}}$</tex-math></inline-formula> from atomic layer deposition and the creation of an undiffused buffer layer between the central measurement area and recombination-active edges. For the latter, both an etch-back approach and a masked diffusion of the boron emitter (sunken emitter) are evaluated. Lifetime measurements and photoluminescence imaging demonstrate that the sunken emitter approach most effectively suppresses edge recombination in small-sized lifetime samples.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 4","pages":"518-522"},"PeriodicalIF":2.5,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nathan Roosloot;Dag Lindholm;Josefine H. Selj;Gaute Otnes
{"title":"Gravimetric Analysis of Edge Sealant Moisture Protection in a Floating Photovoltaic Application","authors":"Nathan Roosloot;Dag Lindholm;Josefine H. Selj;Gaute Otnes","doi":"10.1109/JPHOTOV.2025.3548762","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3548762","url":null,"abstract":"Floating photovoltaic (FPV) modules may face a risk of increased moisture ingress due to their deployment on water surfaces. One way to mitigate this is by using impermeable front- and backsheets, with an edge sealant around the module perimeter. While a suitable sealant should have low bulk permeability, proper sealant application to avoid higher ingress channels at interfaces is crucial. Here, we report on the use of a gravimetric method as a simple way of evaluating moisture ingress through an edge sealant and of identifying application-related issues that lead to increased moisture ingress. The method uses multiple samples that closely mimic the sealant's intended application as part of an FPV design developed by the company Sunlit Sea. Supported by steady-state water vapor transmission rate measurements and finite-element modeling, the method is shown to be capable of determining the order of magnitude of the permeability of two different candidate sealant materials. Moreover, the method detected several application-related sealant failures that were not discernible through visual inspection. Finally, it uncovered potential issues of debonding of one of the sealants in immersion, highlighting a relevant yet understudied stressor for FPV modules.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 3","pages":"442-450"},"PeriodicalIF":2.5,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sisi Wang;Moonyong Kim;Li Wang;Yuchao Zhang;Nathan Chang;Catherine Chan;Brett Hallam
{"title":"Sustainability Impact of Different PV Mounting Systems and Pathways for Decarbonizing Emissions of PV Deployment","authors":"Sisi Wang;Moonyong Kim;Li Wang;Yuchao Zhang;Nathan Chang;Catherine Chan;Brett Hallam","doi":"10.1109/JPHOTOV.2025.3567083","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3567083","url":null,"abstract":"The amount of electricity generated by a solar panel varies according to the installation location and chosen mounting structure. This changes the effective material consumption and the associated effective carbon emissions of electricity produced by each solar panel. This article investigates the impact of different photovoltaic (PV) mounting systems on energy yield, material consumption, and carbon emissions, focusing on the key configurations of fixed-tilt (FT), east–west, and single-axis tracking systems. As global PV capacity rapidly expands, understanding the sustainability of these systems is crucial for decarbonizing the electricity sector. We highlight the impact of different mounting systems on yield at different latitudes and demonstrate that the effective material consumption can vary by over 30% in terms of both g/Wp and g/kWh, along with the impact on carbon emissions in terms of both gCO<sub>2-eq</sub>/Wp and gCO<sub>2-eq</sub>/kWh. Pathways to reduce the carbon footprint in gCO<sub>2-eq</sub>/kWh by up to 60% compared with the FT baseline case are also discussed, including incorporating green steel and decarbonized concrete.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 4","pages":"610-620"},"PeriodicalIF":2.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anica N. Neumann;William E. McMahon;Gavin P. Forcade;Pablo G. Coll;Theresa E. Saenz;Sarah Collins;John Goldsmith;Mariana I. Bertoni;Myles A. Steiner;Emily L. Warren
{"title":"In Situ MOVPE Smoothing of Acoustically Spalled GaAs for Substrate Reuse","authors":"Anica N. Neumann;William E. McMahon;Gavin P. Forcade;Pablo G. Coll;Theresa E. Saenz;Sarah Collins;John Goldsmith;Mariana I. Bertoni;Myles A. Steiner;Emily L. Warren","doi":"10.1109/JPHOTOV.2025.3566754","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3566754","url":null,"abstract":"High material costs, especially for substrates, have limited the widespread adoption of III–V photovoltaics. A potential to reduce this cost is to reuse the III–V substrate via acoustic spalling, however this technique can leave a rough surface, hindering subsequent device performance. This research investigates the potential of using metalorganic vapor-phase epitaxy growth as a buffer layer to smooth the surface of acoustically spalled germanium and gallium arsenide (GaAs) substrates for improved III–V photovoltaic cell yield and performance, while retaining the maximum number of reuses of a substrate. Three potential smoothing layers were explored: lightly doped C:GaAs, highly doped Se:GaInP, and lightly doped Se:GaInP. C:GaAs showed the most promise as a smoothing layer, while Se:GaInP tended to conform to the underlying morphology, potentially increasing roughness in some areas. Utilizing 5 <inline-formula><tex-math>$mu$</tex-math></inline-formula>m of C:GaAs as a planarizing buffer increased the average efficiency (without an antireflection coating) from an as-spalled baseline from 2.1% to 4.9% and performing a 5-min <inline-formula><tex-math>$30^{circ }$</tex-math></inline-formula>C 8:1:1 <inline-formula><tex-math>$mathrm{H_{2}SO_{4}:H_{2}O_{2}:H_{2}O}$</tex-math></inline-formula> etch prior to a 5 <inline-formula><tex-math>$mu$</tex-math></inline-formula>m of C:GaAs as a planarizing buffer further increased efficiency to 11.1%.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 4","pages":"541-548"},"PeriodicalIF":2.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ali Baghban Parashkouh;Ali Sadr;Maryam Heidariramsheh;Nima Taghavinia
{"title":"Improved Lead Halide Perovskite Films and Devices Using Hot-Flow-Assisted Annealing","authors":"Ali Baghban Parashkouh;Ali Sadr;Maryam Heidariramsheh;Nima Taghavinia","doi":"10.1109/JPHOTOV.2025.3546318","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3546318","url":null,"abstract":"Physical and chemical controlling of the lead halide perovskite films is crucial to minimize defects and improve overall performance and stability of perovskite solar cells. In this study, applying a hot flow of dry air on the surface of perovskite films during hot plate annealing is investigated. We found that this technique leads to a smooth texture and reduces the surface defects. A hot dry airflow of 15 L/min improves the power conversion efficiency from 13.56% to 15.31%, with approximately 4.3% and 10.4% enhancement of fill factor and short-circuit current density, respectively. However, increasing the rate of dry airflow leads to large voids, which is a critical concern for leakage current and performance degradation.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 3","pages":"427-433"},"PeriodicalIF":2.5,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Experimental Validation of a Module Cell Cracking Model","authors":"Nick Bosco","doi":"10.1109/JPHOTOV.2025.3542830","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3542830","url":null,"abstract":"The What's Cracking app can predict how changes in crystalline silicon photovoltaic (PV) module materials, design, and mounting affect its susceptibility for cell fracture under uniform loading. This work has experimentally validated the app. A set of commercial crystalline silicon PV modules was obtained for this study. The modules were uniformly loaded at three different mounting points, and their subsequent cell fractures were recorded. A large sample size allowed for the development of an experimental statistical model for cell fracture. The comparison of the experiment to predictions from the app is in excellent agreement. Both experimental and modeling results also elucidate how moving the module mounting points toward the center of the module increases the probability of cell fracture.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 3","pages":"416-419"},"PeriodicalIF":2.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dylan J. Colvin;Andrew M. Gabor;William C. Oltjen;Philip J. Knodle;Ange Dominique Yao;Brent A. Thompson;Nadia Khan;Sina Lotfian;Joseph Raby;Albert Jojo;Xuanji Yu;Max Liggett;Hubert P. Seigneur;Roger H. French;Laura S. Bruckman;Mengjie Li;Kristopher O. Davis
{"title":"Ultraviolet Fluorescence Imaging for Photovoltaic Module Metrology: Best Practices and Survey of Features Observed in Fielded Modules","authors":"Dylan J. Colvin;Andrew M. Gabor;William C. Oltjen;Philip J. Knodle;Ange Dominique Yao;Brent A. Thompson;Nadia Khan;Sina Lotfian;Joseph Raby;Albert Jojo;Xuanji Yu;Max Liggett;Hubert P. Seigneur;Roger H. French;Laura S. Bruckman;Mengjie Li;Kristopher O. Davis","doi":"10.1109/JPHOTOV.2025.3545825","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3545825","url":null,"abstract":"As the photovoltaics (PV) industry grows in sophistication, so must the extent to which systems are characterized. UV Fluorescence (UVF) imaging is a valuable, easy-to-perform, high-throughput, nonintrusive technique for characterizing modules in the field and in the lab. However, UVF is still a relatively new technique, and many in the PV industry are still unaware of its potential. We provide a guideline for obtaining, processing, and interpreting UVF images. We have provided a list of considerations for imaging hardware and settings, a suggested pipeline for image processing, and details on a survey of features shown in UVF images. A new database with UVF images of 7190 modules and another database curated by BrightSpot Automation are publicly available.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 3","pages":"465-477"},"PeriodicalIF":2.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rajiv Daxini;Kevin S. Anderson;Joshua S. Stein;Marios Theristis
{"title":"Photovoltaic Module Spectral Mismatch Losses Due to Cell-Level EQE Variation","authors":"Rajiv Daxini;Kevin S. Anderson;Joshua S. Stein;Marios Theristis","doi":"10.1109/JPHOTOV.2025.3545820","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3545820","url":null,"abstract":"Understanding the impact of variation in the solar spectrum on photovoltaic (PV) device output is critical for accurate and reliable PV performance modeling. While previous studies have examined these spectral effects extensively at the module level, this study examines the spectral impact at the cell level and how subsequent current mismatch can influence module-level output. Cell-level external quantum efficiency (EQE) data from 11 new commercial PV modules are analyzed. The module power output, as determined by the spectral mismatch factor of the module-limiting cell, is computed using the measured cell EQE data in conjunction with gridded meteorological and spectral irradiance data simulated at an approximately 20 <inline-formula><tex-math>$mathbf{mathrm{km}}$</tex-math></inline-formula> resolution across the contiguous USA over one year. This study finds only a small variation in annualized module output of around 0.2% as a result of intramodule EQE variation. However, these losses exhibit significant seasonality, varying by up to around four times the annualized energy difference on a month-to-month basis. The seasonality of the energy loss has implications for subannual PV performance analysis applications such as capacity testing.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 3","pages":"458-464"},"PeriodicalIF":2.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10925465","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Brandon K. Byford;Laura E. Boucheron;Bruce H. King;Jennifer L. Braid
{"title":"Advanced Photovoltaic Module Characterization: Using Image Transformers for Current–Voltage Curve Prediction From Electroluminescence Images","authors":"Brandon K. Byford;Laura E. Boucheron;Bruce H. King;Jennifer L. Braid","doi":"10.1109/JPHOTOV.2025.3562931","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3562931","url":null,"abstract":"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 (<italic>I–V</i>) curve analyzes for identification of damage and power loss. <italic>I–V</i> 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 <italic>I–V</i> curves for PV modules from their corresponding EL images. The predicted <italic>I–V</i> curves allow the accurate prediction of the maximum power point (MPP), short-circuit current <inline-formula><tex-math>$I_{text {sc}}$</tex-math></inline-formula>, and open-circuit voltage <inline-formula><tex-math>$V_{text {oc}}$</tex-math></inline-formula> 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 <inline-formula><tex-math>$R_{text {s}}$</tex-math></inline-formula> demonstrates a mean error of 5.19%, and the photocurrent <inline-formula><tex-math>$I$</tex-math></inline-formula> a mean error of 0.197%. The shunt resistance <inline-formula><tex-math>$R_{text {sh}}$</tex-math></inline-formula> and dark current <inline-formula><tex-math>$I_{text {o}}$</tex-math></inline-formula> parameters are predicted with larger errors because of their sensitivity to small changes in the <italic>I–V</i> curve.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 4","pages":"557-565"},"PeriodicalIF":2.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11002587","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Khaled Alnuaimi;Ameena Saad Al-Sumaiti;Mohamad Alansari;Huai Wang;Khalifa Hassan Al Hosani
{"title":"Deep Learning-Based Health Monitoring for Photovoltaic Systems","authors":"Khaled Alnuaimi;Ameena Saad Al-Sumaiti;Mohamad Alansari;Huai Wang;Khalifa Hassan Al Hosani","doi":"10.1109/JPHOTOV.2025.3563887","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3563887","url":null,"abstract":"The transition to renewable energy sources like photovoltaic (PV) systems is essential for societal progress, counteracting the adverse effects of fossil fuels. However, managing PV systems entails significant challenges and economic implications. PV fault occurrence necessitates swift detection and resolution, exacerbating financial burdens. Effective fault diagnosis relies heavily on data from PV plant monitoring and energy management systems. Historically, PV monitoring relied on manual inspections, but autonomous aerial vehicle (UAV) technology provides a more efficient and comprehensive solution, enhancing safety and offering detailed imagery, scalability, environmental monitoring, and advanced data analytics. This study utilizes deep learning (DL) approaches to monitor the health of the PV, focusing on analyzing UAV-captured scenes. Specifically, this article presents an end-to-end two-stage DL-based health monitoring framework that consists of semantic segmentation model, SegFormer, for isolating solar panels and object detection model, YOLOv8, for identifying anomalies within the PV modules. The proposed framework is validated and compared with state-of-the-art (SOTA) models on a three publicly available UAV-captured datasets. Results show improvements of 25.8% and 1.5% in solar panel segmentation, and 26.6% in solar panel anomaly detection compared with recent SOTA models.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 4","pages":"577-592"},"PeriodicalIF":2.5,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}