Pub Date : 2024-03-02DOI: 10.1109/JPHOTOV.2024.3388168
Mingda Yang;Wasim Javed;Bing Guo;Jim Ji
Solar energy from solar photovoltaics (PV) has become a rapidly growing sustainable energy source around the world. However, maintaining PV system efficiency remains a challenging problem. In desert regions, soiling is one of the most significant environmental factors that can cause PV system loss. In our early work, a PV soiling loss estimation method based on a single-image feature and in-lab testing was developed. In this study, we extend our previous work by incorporating various image features in a machine-learning regression model to predict PV soiling loss. The new model is trained and tested using PV performance data and RAW panel images collected in the field over several months, covering real-time soiling loss levels up to about 28%. There are 479 RAW images with 21 unique soiling loss levels, which were taken under different camera settings. The results show that the new method can reliably predict the soiling loss when the images are taken under similar settings as the training data ( R -squared value of 0.98 and normalized RMSE is 0.01 for the training dataset).
太阳能光伏发电（PV）已成为全球快速增长的可持续能源。然而，保持光伏系统的效率仍然是一个具有挑战性的问题。在沙漠地区，污损是导致光伏系统损耗的最主要环境因素之一。在我们早期的工作中，开发了一种基于单图像特征和实验室测试的光伏污损估计方法。在本研究中，我们扩展了之前的工作，将各种图像特征纳入机器学习回归模型，以预测光伏污损。我们使用光伏性能数据和几个月来在现场收集的 RAW 面板图像对新模型进行了训练和测试，涵盖了最高约 28% 的实时污损水平。共有 479 张 RAW 图像，21 个独特的污损等级，这些图像是在不同的相机设置下拍摄的。结果表明，当图像是在与训练数据相似的设置下拍摄时，新方法可以可靠地预测脏污损失（训练数据集的 R 平方值为 0.98，归一化 RMSE 为 0.01）。
{"title":"Estimating PV Soiling Loss Using Panel Images and a Feature-Based Regression Model","authors":"Mingda Yang;Wasim Javed;Bing Guo;Jim Ji","doi":"10.1109/JPHOTOV.2024.3388168","DOIUrl":"10.1109/JPHOTOV.2024.3388168","url":null,"abstract":"Solar energy from solar photovoltaics (PV) has become a rapidly growing sustainable energy source around the world. However, maintaining PV system efficiency remains a challenging problem. In desert regions, soiling is one of the most significant environmental factors that can cause PV system loss. In our early work, a PV soiling loss estimation method based on a single-image feature and in-lab testing was developed. In this study, we extend our previous work by incorporating various image features in a machine-learning regression model to predict PV soiling loss. The new model is trained and tested using PV performance data and RAW panel images collected in the field over several months, covering real-time soiling loss levels up to about 28%. There are 479 RAW images with 21 unique soiling loss levels, which were taken under different camera settings. The results show that the new method can reliably predict the soiling loss when the images are taken under similar settings as the training data (\u0000<italic>R</i>\u0000-squared value of 0.98 and normalized RMSE is 0.01 for the training dataset).","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"14 4","pages":"661-668"},"PeriodicalIF":2.5,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140831426","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}
New massive markets for space multijunction solar cells are being discussed globally. For such an explosive increase in demand to materialize, a more sustainable and affordable Ge substrate technology is required. To this end, lithography-based Ge-on-Nothing and electrochemical process-based porous Ge wafers were developed. Both approaches yield uniform and smooth monocrystalline Ge-on-Ge engineered substrates after annealing, of which the top layer is weakly attached to the parent substrate. High-quality space solar cells were grown on them, followed by successful foil detachment and surface reconditioning. These results clearly demonstrate the feasibility of the reusable Ge substrate concept.
全球正在讨论空间多接面太阳能电池的新的巨大市场。要实现这种爆炸性的需求增长，需要一种更可持续、更经济实惠的 Ge 衬底技术。为此，我们开发了基于光刻技术的 "Ge-on-Nothing "和基于电化学工艺的多孔地质晶片。这两种方法都能在退火后产生均匀、光滑的单晶 "Ge-on-Ge "工程衬底，其顶层与母衬底的附着力很弱。在这些基底上生长出了高质量的空间太阳能电池，随后成功地进行了箔分离和表面修复。这些结果清楚地证明了可重复使用的 Ge 衬底概念的可行性。
{"title":"Overview of Engineered Germanium Substrate Development for Affordable Large-Volume Multijunction Solar Cells","authors":"Jinyoun Cho;Valérie Depauw;Alexandre Chapotot;Waldemar Schreiber;Tadeáš Hanuš;Nicolas Paupy;Valentin Daniel;Guillaume Courtois;Bouraoui Ilahi;Abderraouf Boucherif;Clément Porret;Roger Loo;Jens Ohlmann;Stefan Janz;Kristof Dessein","doi":"10.1109/JPHOTOV.2024.3390846","DOIUrl":"10.1109/JPHOTOV.2024.3390846","url":null,"abstract":"New massive markets for space multijunction solar cells are being discussed globally. For such an explosive increase in demand to materialize, a more sustainable and affordable Ge substrate technology is required. To this end, lithography-based Ge-on-Nothing and electrochemical process-based porous Ge wafers were developed. Both approaches yield uniform and smooth monocrystalline Ge-on-Ge engineered substrates after annealing, of which the top layer is weakly attached to the parent substrate. High-quality space solar cells were grown on them, followed by successful foil detachment and surface reconditioning. These results clearly demonstrate the feasibility of the reusable Ge substrate concept.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"14 4","pages":"623-628"},"PeriodicalIF":2.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140831429","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}
Pub Date : 2024-02-28DOI: 10.1109/JPHOTOV.2024.3366666
Joseph Ranalli;William B. Hobbs
Large-scale photovoltaic plants collect monitoring and operational data at various spatial scales within the plant (e.g., strings, combiners, and inverters). Manual validation of the spatial position of these plant segments relative to the plant design requires on-site observations that may be prohibitively costly or labor intensive. This article presents a methodology for validating plant segment position based on operational data from the plant. By observing the delay between segment responses to cloud motion, predictions of their relative positions within the plant can be made. The method was demonstrated on combiner-level data from a 20-MW, operational photovoltaic plant in the United States. Several instances of apparently mislabeled combiners were identified from the analysis. A partial validation of 20 combiners was conducted by inspecting the plant, with results showing complete agreement between observation and predictions. Predictions derived from this methodology can serve as the basis for further plant inspection and corrective maintenance.
{"title":"PV Plant Equipment Labels and Layouts Can Be Validated by Analyzing Cloud Motion in Existing Plant Measurements","authors":"Joseph Ranalli;William B. Hobbs","doi":"10.1109/JPHOTOV.2024.3366666","DOIUrl":"10.1109/JPHOTOV.2024.3366666","url":null,"abstract":"Large-scale photovoltaic plants collect monitoring and operational data at various spatial scales within the plant (e.g., strings, combiners, and inverters). Manual validation of the spatial position of these plant segments relative to the plant design requires on-site observations that may be prohibitively costly or labor intensive. This article presents a methodology for validating plant segment position based on operational data from the plant. By observing the delay between segment responses to cloud motion, predictions of their relative positions within the plant can be made. The method was demonstrated on combiner-level data from a 20-MW, operational photovoltaic plant in the United States. Several instances of apparently mislabeled combiners were identified from the analysis. A partial validation of 20 combiners was conducted by inspecting the plant, with results showing complete agreement between observation and predictions. Predictions derived from this methodology can serve as the basis for further plant inspection and corrective maintenance.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"14 3","pages":"538-548"},"PeriodicalIF":3.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10453268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140001640","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}
Pub Date : 2024-02-23DOI: 10.1109/JPHOTOV.2024.3364811
Seong-Hyeon Ahn;Gyu Gwang Kim;Jin Ho Choi;Yeong-Beom Kang;Jin Hee Hyun;Hyung-Keun Ahn
Environmental factors that influence marine photovoltaic (PV) systems differ considerably from those affecting overland PV systems. The bird dropping, which is one of them, is a main cause of marine PV power reduction. The power prediction of a PV module contaminated by bird droppings is hard because light could pass through the bird dropping depending on the thickness of it unlike hard shading condition. This article shows differences between a bird-dropping shading and hard shading of the contaminated modules, and suggests a prediction model considering a transmittance of a bird dropping using image processing (IP). First, the shading rate (SR), which is the PV module surface ratio covered by bird droppings, is calculated using IP technologies. The accuracy of the SR is verified through an experiment creating artificial hard shading on the module using black masking tape. It has an error rate of 3% compared to our targeted benchmark SRs. Subsequently, an equivalent step methodology, which combines IV curves from individual cells to yield an IV curve for the entire PV module, was adopted to predict the power. The accuracy of this methodology is confirmed, and it showed a 5% error. However, in the case of real bird-dropping shades, discrepancies up to 15% are observed. In this article, the light penetration through bird droppings by a transmittance function was considered to predict the output of modules contaminated by the bird droppings. Errors fluctuate between 1%–20% based on the quantification of light interferences.
影响海洋光伏（PV）系统的环境因素与影响陆地光伏系统的环境因素有很大不同。鸟粪是其中之一，也是导致海洋光伏发电功率下降的主要原因。被鸟粪污染的光伏模块很难预测功率，因为光线可以穿过鸟粪，这取决于鸟粪的厚度，与硬遮挡条件不同。本文说明了鸟粪遮挡与硬遮挡污染组件之间的差异，并提出了一个利用图像处理（IP）考虑鸟粪透射率的预测模型。首先，利用 IP 技术计算了遮光率（SR），即被鸟粪覆盖的光伏组件表面比率。通过使用黑色遮蔽胶带在组件上制造人工硬阴影的实验，验证了 SR 的准确性。与我们的目标基准 SR 相比，其误差率为 3%。随后，我们采用了等效阶跃方法来预测功率，该方法将单个电池的 IV 曲线与整个光伏模块的 IV 曲线相结合。该方法的准确性得到了证实，误差仅为 5%。然而，在真实的鸟落遮阳板情况下，观察到的误差高达 15%。在本文中，利用透射率函数考虑了鸟粪的透光率，以预测被鸟粪污染的组件的输出功率。根据光干扰的量化，误差在 1%-20%之间波动。
{"title":"Power Prediction of PV Modules Contaminated by Bird Droppings via an Image Thresholding Process","authors":"Seong-Hyeon Ahn;Gyu Gwang Kim;Jin Ho Choi;Yeong-Beom Kang;Jin Hee Hyun;Hyung-Keun Ahn","doi":"10.1109/JPHOTOV.2024.3364811","DOIUrl":"10.1109/JPHOTOV.2024.3364811","url":null,"abstract":"Environmental factors that influence marine photovoltaic (PV) systems differ considerably from those affecting overland PV systems. The bird dropping, which is one of them, is a main cause of marine PV power reduction. The power prediction of a PV module contaminated by bird droppings is hard because light could pass through the bird dropping depending on the thickness of it unlike hard shading condition. This article shows differences between a bird-dropping shading and hard shading of the contaminated modules, and suggests a prediction model considering a transmittance of a bird dropping using image processing (IP). First, the shading rate (SR), which is the PV module surface ratio covered by bird droppings, is calculated using IP technologies. The accuracy of the SR is verified through an experiment creating artificial hard shading on the module using black masking tape. It has an error rate of 3% compared to our targeted benchmark SRs. Subsequently, an equivalent step methodology, which combines IV curves from individual cells to yield an IV curve for the entire PV module, was adopted to predict the power. The accuracy of this methodology is confirmed, and it showed a 5% error. However, in the case of real bird-dropping shades, discrepancies up to 15% are observed. In this article, the light penetration through bird droppings by a transmittance function was considered to predict the output of modules contaminated by the bird droppings. Errors fluctuate between 1%–20% based on the quantification of light interferences.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"14 3","pages":"557-568"},"PeriodicalIF":3.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10443925","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946436","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}
Pub Date : 2024-02-21DOI: 10.1109/JPHOTOV.2024.3364823
David L. Young;Timothy J. Silverman;Nicholas P. Irvin;Daniel Huerta-Murillo;Bill Holtkamp;Nick Bosco
This article explores the use of femtosecond (fs) lasers to form glass-to-glass welds for hermetically sealed, polymer-free solar modules. Low-iron solar glass coupons were welded together without the use of glass filler using a fs laser with dedicated optics to elongate the focal plane parallel to the incident beam. The resulting welds were then stress tested to failure to reveal the critical stress intensity factor, KIc . These values were used in a structural mechanics model of a 1 m × 2 m glass/glass module under a simulated static load test. The results show that the fs laser welds are strong enough for a suitably framed module to pass the IEC 61215 static load test with a load of 5400 Pa. Key to this finding is that the module must be framed and braced, and the glass must be ribbed to allow pockets for the cells and welds inside the border of the module. The result is a module design that is completely polymer free, hermetically sealed, has improved thermal properties, and is easily recycled.
本文探讨了如何利用飞秒（fs）激光形成玻璃对玻璃的焊缝，用于密封的无聚合物太阳能模块。在不使用玻璃填料的情况下，使用带有专用光学器件的飞秒激光将低铁太阳能玻璃试样焊接在一起，以拉长平行于入射光束的焦平面。然后对焊缝进行失效应力测试，以揭示临界应力强度因子 KIc。这些数值被用于 1 m × 2 m 玻璃/玻璃模块在模拟静载荷测试下的结构力学模型。结果表明，fs 激光焊缝的强度足以让框架合适的模块通过 IEC 61215 静态负载测试，负载为 5400 Pa。这一结果的关键在于，模块必须有框架和支撑，玻璃必须有棱纹，以便在模块边界内为电池片和焊缝留出空间。因此，这种模块设计完全不含聚合物，密封性好，热性能更佳，而且易于回收利用。
{"title":"Towards Polymer-Free, Femto-Second Laser-Welded Glass/Glass Solar Modules","authors":"David L. Young;Timothy J. Silverman;Nicholas P. Irvin;Daniel Huerta-Murillo;Bill Holtkamp;Nick Bosco","doi":"10.1109/JPHOTOV.2024.3364823","DOIUrl":"10.1109/JPHOTOV.2024.3364823","url":null,"abstract":"This article explores the use of femtosecond (fs) lasers to form glass-to-glass welds for hermetically sealed, polymer-free solar modules. Low-iron solar glass coupons were welded together without the use of glass filler using a fs laser with dedicated optics to elongate the focal plane parallel to the incident beam. The resulting welds were then stress tested to failure to reveal the critical stress intensity factor, \u0000<italic>K<sub>Ic</sub></i>\u0000. These values were used in a structural mechanics model of a 1 m × 2 m glass/glass module under a simulated static load test. The results show that the fs laser welds are strong enough for a suitably framed module to pass the IEC 61215 static load test with a load of 5400 Pa. Key to this finding is that the module must be framed and braced, and the glass must be ribbed to allow pockets for the cells and welds inside the border of the module. The result is a module design that is completely polymer free, hermetically sealed, has improved thermal properties, and is easily recycled.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"14 3","pages":"497-502"},"PeriodicalIF":3.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946439","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}
Pub Date : 2024-02-20DOI: 10.1109/JPHOTOV.2024.3364409
{"title":"IEEE Journal of Photovoltaics Information for Authors","authors":"","doi":"10.1109/JPHOTOV.2024.3364409","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2024.3364409","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"14 2","pages":"C3-C3"},"PeriodicalIF":3.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10440382","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139937081","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}
Pub Date : 2024-02-20DOI: 10.1109/JPHOTOV.2024.3365769
{"title":"IEEE Open Access Publishing","authors":"","doi":"10.1109/JPHOTOV.2024.3365769","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2024.3365769","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"14 2","pages":"372-372"},"PeriodicalIF":3.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10440378","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139915572","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}
Pub Date : 2024-02-16DOI: 10.1109/JPHOTOV.2024.3362134
Thiago Mota Vieira;Ézio C. Santana;Tarso V. Ferreira;Douglas B. Riffel
The electrical energy produced by photovoltaic systems can be critically affected by a variety of factors. In order to detect defective photovoltaic cells, several monitoring techniques, such as lock-in thermography, have been widely used alongside some analytical methods that avoid subjectivity. This article proposes a method with low computational cost that provides a simple and easily implementable way to quantifiably discern if a photovoltaic cell is defective or not. A two-dimensional Gaussian fit is applied to images generated by fast Fourier transform and principal component analysis algorithms on thermographic data from lock-in thermography tests. The considered coefficient of determination ${R}^2$ was found to be a good measure of fitting quality. Additionally, the method highlighted the potential of its application on first principal component, with ${R}^2$ between 0.944 and 0.986, and magnitude images, with ${R}^2$ between 0.965 and 0.985, in order to identify and distinguish nondefective cells from defective ones.
{"title":"Photovoltaic Cell Defect Detection by Lock-In Thermography Using 2-D Gaussian Profile","authors":"Thiago Mota Vieira;Ézio C. Santana;Tarso V. Ferreira;Douglas B. Riffel","doi":"10.1109/JPHOTOV.2024.3362134","DOIUrl":"10.1109/JPHOTOV.2024.3362134","url":null,"abstract":"The electrical energy produced by photovoltaic systems can be critically affected by a variety of factors. In order to detect defective photovoltaic cells, several monitoring techniques, such as lock-in thermography, have been widely used alongside some analytical methods that avoid subjectivity. This article proposes a method with low computational cost that provides a simple and easily implementable way to quantifiably discern if a photovoltaic cell is defective or not. A two-dimensional Gaussian fit is applied to images generated by fast Fourier transform and principal component analysis algorithms on thermographic data from lock-in thermography tests. The considered coefficient of determination \u0000<inline-formula><tex-math>${R}^2$</tex-math></inline-formula>\u0000 was found to be a good measure of fitting quality. Additionally, the method highlighted the potential of its application on first principal component, with \u0000<inline-formula><tex-math>${R}^2$</tex-math></inline-formula>\u0000 between 0.944 and 0.986, and magnitude images, with \u0000<inline-formula><tex-math>${R}^2$</tex-math></inline-formula>\u0000 between 0.965 and 0.985, in order to identify and distinguish nondefective cells from defective ones.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"14 3","pages":"480-487"},"PeriodicalIF":3.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139956672","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}
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