Pub Date : 2025-10-28DOI: 10.1109/JPHOTOV.2025.3626191
{"title":"2025 Index IEEE Journal of Photovoltaics","authors":"","doi":"10.1109/JPHOTOV.2025.3626191","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3626191","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 6","pages":"995-1026"},"PeriodicalIF":2.6,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11219677","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405266","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 : 2025-10-22DOI: 10.1109/JPHOTOV.2025.3621371
{"title":"Call for Papers for a Special Issue of IEEE Transactions on Electron Devices on “Reliability of Advanced Nodes”","authors":"","doi":"10.1109/JPHOTOV.2025.3621371","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3621371","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 6","pages":"993-994"},"PeriodicalIF":2.6,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11214299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145339711","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 : 2025-10-22DOI: 10.1109/JPHOTOV.2025.3621367
{"title":"IEEE Journal of Photovoltaics Information for Authors","authors":"","doi":"10.1109/JPHOTOV.2025.3621367","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3621367","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 6","pages":"C3-C3"},"PeriodicalIF":2.6,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11214302","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145339692","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 : 2025-10-22DOI: 10.1109/JPHOTOV.2025.3621369
{"title":"Call for Papers for a Special Issue of IEEE Transactions on Electron Devices on “Ultrawide Band Gap Semiconductor Device for RF, Power and Optoelectronic Applications”","authors":"","doi":"10.1109/JPHOTOV.2025.3621369","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3621369","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 6","pages":"991-992"},"PeriodicalIF":2.6,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11214309","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145339687","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 : 2025-10-22DOI: 10.1109/JPHOTOV.2025.3620446
{"title":"Golden List of Reviewers","authors":"","doi":"10.1109/JPHOTOV.2025.3620446","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3620446","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 6","pages":"988-990"},"PeriodicalIF":2.6,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11214300","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145339715","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 : 2025-10-22DOI: 10.1109/JPHOTOV.2025.3608474
Tajrian Mollick;Md Jobayer;Md. Samrat Hossin;Shahidul Islam Khan;A. S. Nazmul Huda;Saifur Rahman Sabuj
Solar energy adoption is rapidly growing as a sustainable option, with solar panels used on residential buildings, commercial properties, and large-scale farms. However, the unpredictable nature of solar power can lead to suboptimal energy generation from photovoltaic (PV) panels. Despite the high effectiveness of deep learning (DL) models in forecasting PV power, they often struggle with the perception of being “closed boxes” that lack clear explanations for their prediction results, which fail to highlight the key features for PV prediction. To address the critical issue of full transparency, this study explores a well-known DL model named lightweight deep neural network (LWDNN) in PV power forecasting, along with the application of explainable artificial intelligence (XAI) tools like Shapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME). Real-time data collected from a grid-connected solar PV system located in Dhaka were utilized to perform the prediction. By enabling XAI model interpretation, we identified feature contributions and explained individual predictions, reducing training computational demands without compromising accuracy. The reliability of the LWDNN model is assessed using both complete and reduced feature sets through performance metrics such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The test results show that the proposed LWDNN model based on SHAP analysis outperforms conventional schemes by achieving RMSE = 6.180 kW, MAE = 1.939 kW, and R2 = 0.988. Finally, the model was implemented on a Raspberry Pi for low-power solar forecasting, demonstrating the feasibility of edge deployment.
{"title":"An Interpretable Deep Learning Model for Solar Power Generation Forecasting in a Grid-Connected Hybrid Solar System","authors":"Tajrian Mollick;Md Jobayer;Md. Samrat Hossin;Shahidul Islam Khan;A. S. Nazmul Huda;Saifur Rahman Sabuj","doi":"10.1109/JPHOTOV.2025.3608474","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3608474","url":null,"abstract":"Solar energy adoption is rapidly growing as a sustainable option, with solar panels used on residential buildings, commercial properties, and large-scale farms. However, the unpredictable nature of solar power can lead to suboptimal energy generation from photovoltaic (PV) panels. Despite the high effectiveness of deep learning (DL) models in forecasting PV power, they often struggle with the perception of being “closed boxes” that lack clear explanations for their prediction results, which fail to highlight the key features for PV prediction. To address the critical issue of full transparency, this study explores a well-known DL model named lightweight deep neural network (LWDNN) in PV power forecasting, along with the application of explainable artificial intelligence (XAI) tools like Shapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME). Real-time data collected from a grid-connected solar PV system located in Dhaka were utilized to perform the prediction. By enabling XAI model interpretation, we identified feature contributions and explained individual predictions, reducing training computational demands without compromising accuracy. The reliability of the LWDNN model is assessed using both complete and reduced feature sets through performance metrics such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R<sup>2</sup>). The test results show that the proposed LWDNN model based on SHAP analysis outperforms conventional schemes by achieving RMSE = 6.180 kW, MAE = 1.939 kW, and R<sup>2</sup> = 0.988. Finally, the model was implemented on a Raspberry Pi for low-power solar forecasting, demonstrating the feasibility of edge deployment.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 6","pages":"941-954"},"PeriodicalIF":2.6,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145339706","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}
Floating photovoltaics (FPVs) are gaining traction as a land-saving alternative to ground-mounted photovoltaics (GPV). A commonly cited advantage of FPVs is their potential for lower operating temperatures due to the cooling effect of water. However, existing literature shows that the thermal performance of FPV systems may not consistently exceed that of GPV systems, as it is influenced by technology and location. Consequently, studying the thermal properties of a range of FPV systems is crucial to optimize power output and enable accurate energy yield modeling for new sites. This work investigates the thermal properties and calculated heat loss coefficients, or U-values, associated with the Faiman model for an FPV system using Ciel & Terre's Hydrelio Air floats, located in a pond in South Africa. A dependence of U-values on wind direction was observed, with improved cooling when the wind approaches from the rear side of the system. The estimated U-value components were U0 = 21.6 W/m2·K and U1 = 3.60 W·s/m3·K for wind from the front and U0 = 19.4 W/m2·K and U1 = 7.10 W·s/m3·K for wind from the rear side. The impact of the observed cooling variation due to wind direction on system performance was also evaluated, revealing a 1.7% increase in median performance ratio when the wind originates from the rear side.
{"title":"Impact of Wind Speed and Direction on Cooling of a Pontoon-Based Floating Photovoltaic System","authors":"Vilde Stueland Nysted;Torunn Kjeldstad;Dag Lindholm;Marit Sandsaunet Ulset;Josefine Selj","doi":"10.1109/JPHOTOV.2025.3611425","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3611425","url":null,"abstract":"Floating photovoltaics (FPVs) are gaining traction as a land-saving alternative to ground-mounted photovoltaics (GPV). A commonly cited advantage of FPVs is their potential for lower operating temperatures due to the cooling effect of water. However, existing literature shows that the thermal performance of FPV systems may not consistently exceed that of GPV systems, as it is influenced by technology and location. Consequently, studying the thermal properties of a range of FPV systems is crucial to optimize power output and enable accurate energy yield modeling for new sites. This work investigates the thermal properties and calculated heat loss coefficients, or <italic>U</i>-values, associated with the Faiman model for an FPV system using Ciel & Terre's Hydrelio Air floats, located in a pond in South Africa. A dependence of <italic>U</i>-values on wind direction was observed, with improved cooling when the wind approaches from the rear side of the system. The estimated <italic>U</i>-value components were <italic>U</i><sub>0</sub> = 21.6 W/m<sup>2</sup>·K and <italic>U</i><sub>1</sub> = 3.60 W·s/m<sup>3</sup>·K for wind from the front and <italic>U</i><sub>0</sub> = 19.4 W/m<sup>2</sup>·K and <italic>U</i><sub>1</sub> = 7.10 W·s/m<sup>3</sup>·K for wind from the rear side. The impact of the observed cooling variation due to wind direction on system performance was also evaluated, revealing a 1.7% increase in median performance ratio when the wind originates from the rear side.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 6","pages":"955-964"},"PeriodicalIF":2.6,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145341070","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 : 2025-10-03DOI: 10.1109/JPHOTOV.2025.3611430
Szymon Pelczar
The land equivalent ratio (LER) is a widely used coefficient by researchers in studies related to agrivoltaic systems. Although the indicator mentioned was developed to determine the benefits of intercropping, it has also been found useful for evaluating agrivoltaic installations. The objective of the LER is to describe the effectiveness of land use under agrivoltaic conditions versus conventional conditions, which implies separate production of crops and electricity. However, the mentioned coefficient does not give a complete description of an agrivoltaic system and its performance. To do so, additional indicators are developed. This study aims to demonstrate that additional parameters, which can be used to better describe an agrivoltaic system in terms of its comparison with conventional conditions. The coefficients presented can help assess the validity of agrivoltaic implementation and to make the decision whether, considering given conditions, it is more desirable to realize a conventional photovoltaic power plant or an agrivoltaic one.
{"title":"Is the Land Equivalent Ratio (LER) a Sufficient Indicator to Describe the Efficiency of Agrivoltaic System?","authors":"Szymon Pelczar","doi":"10.1109/JPHOTOV.2025.3611430","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3611430","url":null,"abstract":"The land equivalent ratio (LER) is a widely used coefficient by researchers in studies related to agrivoltaic systems. Although the indicator mentioned was developed to determine the benefits of intercropping, it has also been found useful for evaluating agrivoltaic installations. The objective of the LER is to describe the effectiveness of land use under agrivoltaic conditions versus conventional conditions, which implies separate production of crops and electricity. However, the mentioned coefficient does not give a complete description of an agrivoltaic system and its performance. To do so, additional indicators are developed. This study aims to demonstrate that additional parameters, which can be used to better describe an agrivoltaic system in terms of its comparison with conventional conditions. The coefficients presented can help assess the validity of agrivoltaic implementation and to make the decision whether, considering given conditions, it is more desirable to realize a conventional photovoltaic power plant or an agrivoltaic one.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 6","pages":"977-983"},"PeriodicalIF":2.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145339600","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 : 2025-09-17DOI: 10.1109/JPHOTOV.2025.3608480
Ushnik Chakrabarti;Binoy Kumar Karmakar
Standard protection devices, such as overcurrent protection devices (OCPD) or ground fault protection devices (GFPD), fail to detect faults due to the presence of series blocking diodes in a series–parallel configured solar photovoltaic (PV) array. This is because, the blocking diode limits the fault current below the respective threshold of the OCPD or GFPD fuses. Several techniques are available in the literature, which attempt to overcome the ineffectiveness of the protection devices in the presence of series blocking diodes. However, the common limitation of these techniques are that they fail to distinguish a fault from partial shading conditions. This can lead to false positives affecting productivity. To overcome the shortcomings of the available techniques, this work proposes a string current correlation-based fault detection technique for PV arrays, which is also effective under partial shading conditions. This work also computes a threshold value of the anticorrelation between the string currents that separates faults from partial shading. MATLAB simulations considering various fault types and weather conditions show its effectiveness in detecting faults and separating it from partial shading. A small-scale hardware set-up is also developed to establish the applicability of the proposed technique in a real-world scenario.
{"title":"An Efficient String Current Correlation-Based PV Array Fault Detection Technique","authors":"Ushnik Chakrabarti;Binoy Kumar Karmakar","doi":"10.1109/JPHOTOV.2025.3608480","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3608480","url":null,"abstract":"Standard protection devices, such as overcurrent protection devices (OCPD) or ground fault protection devices (GFPD), fail to detect faults due to the presence of series blocking diodes in a series–parallel configured solar photovoltaic (PV) array. This is because, the blocking diode limits the fault current below the respective threshold of the OCPD or GFPD fuses. Several techniques are available in the literature, which attempt to overcome the ineffectiveness of the protection devices in the presence of series blocking diodes. However, the common limitation of these techniques are that they fail to distinguish a fault from partial shading conditions. This can lead to false positives affecting productivity. To overcome the shortcomings of the available techniques, this work proposes a string current correlation-based fault detection technique for PV arrays, which is also effective under partial shading conditions. This work also computes a threshold value of the anticorrelation between the string currents that separates faults from partial shading. MATLAB simulations considering various fault types and weather conditions show its effectiveness in detecting faults and separating it from partial shading. A small-scale hardware set-up is also developed to establish the applicability of the proposed technique in a real-world scenario.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 6","pages":"932-940"},"PeriodicalIF":2.6,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145339705","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}
Screen-printable copper (Cu) paste offers a promising, cost-effective plug-and-play alternative for photovoltaic cell metallization. However, the tendency of Cu diffusion into silicon presents a key challenge in maintaining cell performance. This work reports on the use of Bert Thin Films’ screen-printable Cu paste in combination with a postfabrication laser-enhanced contact optimization (LECO) process to significantly improve the stability and performance of Cu-contacted passivated emitter and rear contact (PERC) solar cells. Ag-free Cu-contacted p-PERC solar cell efficiency of 21.4% was achieved with a low series resistance of 0.7 Ω-cm2 and a fill factor of 79% after the LECO process, which remained essentially stable over 17 days. In addition, LECO-treated cells showed a pseudofill factor (pFF) of 82.4% compared to 80.7% for the untreated cells, indicating that the LECO process not only reduces contact resistance but also mitigates Cu migration toward the junction. The LECO process enables low-temperature firing by restoring the series resistance. Under firing the Cu-contacted screen-printed cells improves the pFF but results in high series resistance and low cell efficiency before the LECO treatment. In contrast, cells without LECO treatment showed an efficiency of 10.7% on day one, which increased to 19.4% after 17 days due to the reduction in series resistance from 9.3 to 1.8 Ω-cm2. This study shows that the synergy between Bert Thin Films’ Cu paste and the LECO treatment significantly narrows the efficiency gap between Cu and Ag-contacted p-PERC cells, paving the way for scalable, high-efficiency, Ag-free solar cells.
{"title":"High Efficiency Screen-Printed Ag-Free PERC Solar Cell With Cu Paste and Laser-Enhanced Contact Optimization","authors":"Ruohan Zhong;Venkata Sai Aditya Mulkaluri;Kevin Elmer;Vijaykumar Upadhyaya;Young Woo Ok;Ruvini Dharmadasa;Erin Yenney;Apolo Nambo;Thad Druffel;Ajeet Rohatgi","doi":"10.1109/JPHOTOV.2025.3597679","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3597679","url":null,"abstract":"Screen-printable copper (Cu) paste offers a promising, cost-effective plug-and-play alternative for photovoltaic cell metallization. However, the tendency of Cu diffusion into silicon presents a key challenge in maintaining cell performance. This work reports on the use of Bert Thin Films’ screen-printable Cu paste in combination with a postfabrication laser-enhanced contact optimization (LECO) process to significantly improve the stability and performance of Cu-contacted passivated emitter and rear contact (PERC) solar cells. Ag-free Cu-contacted p-PERC solar cell efficiency of 21.4% was achieved with a low series resistance of 0.7 Ω-cm<sup>2</sup> and a fill factor of 79% after the LECO process, which remained essentially stable over 17 days. In addition, LECO-treated cells showed a pseudofill factor (pFF) of 82.4% compared to 80.7% for the untreated cells, indicating that the LECO process not only reduces contact resistance but also mitigates Cu migration toward the junction. The LECO process enables low-temperature firing by restoring the series resistance. Under firing the Cu-contacted screen-printed cells improves the pFF but results in high series resistance and low cell efficiency before the LECO treatment. In contrast, cells without LECO treatment showed an efficiency of 10.7% on day one, which increased to 19.4% after 17 days due to the reduction in series resistance from 9.3 to 1.8 Ω-cm<sup>2</sup>. This study shows that the synergy between Bert Thin Films’ Cu paste and the LECO treatment significantly narrows the efficiency gap between Cu and Ag-contacted p-PERC cells, paving the way for scalable, high-efficiency, Ag-free solar cells.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 6","pages":"984-987"},"PeriodicalIF":2.6,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145341061","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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