Pub Date : 2024-11-04DOI: 10.1109/JPHOTOV.2024.3489253
{"title":"2024 Index IEEE Journal of Photovoltaics Vol. 14","authors":"","doi":"10.1109/JPHOTOV.2024.3489253","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2024.3489253","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"14 6","pages":"977-1002"},"PeriodicalIF":2.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10741899","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579156","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 : 2024-10-31DOI: 10.1109/JPHOTOV.2024.3483260
Mohammed Dahesh;Mohammed Al-Matwakel;Marwan Dhamrin
In this article, a degradation analysis of seventeen 38-year-old PV modules is conducted to estimate the degradation rates of P Peak , I SC , V OC , and fill factor, and identify the degradation modes that affected these modules. The modules under investigation were degraded under two different conditions: 16 modules were operated in the field, for 38 years, as a part of an off-grid photovoltaic system that was installed on the roof of the Faculty of Science, Sana'a University in Yemen, and one module was stored in a warehouse for the same period (exposure period<48>I – V curve measurement, infrared thermal imaging, electroluminescence imaging, and insulation test have been carried out for each module. Upon comparing with reference values as given by the manufacturer, the median peak power degradation rate of the field-exposed modules over the outdoor exposure period was found to be 25.37%. While the peak power degradation rate of the warehoused module was found to be 14.30%. Encapsulant delamination from cells along grid fingers, corrosion of cell fingers, and hot spots were detected in the warehoused module. On the other hand, encapsulant discoloration, shunting defect, humidity corrosion, front delamination, cell fingers corrosion, interconnect ribbons corrosion, hot spots, and finger interruptions were the principal causes of performance degradation of the field-exposed modules. A description of the detected degradation modes includes a brief discussion of the limitations and benefits of the design of these modules.
{"title":"Degradation Analysis of 38-Year-Old PV Modules Under the Weather Conditions of Sana'a-Yemen","authors":"Mohammed Dahesh;Mohammed Al-Matwakel;Marwan Dhamrin","doi":"10.1109/JPHOTOV.2024.3483260","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2024.3483260","url":null,"abstract":"In this article, a degradation analysis of seventeen 38-year-old PV modules is conducted to estimate the degradation rates of \u0000<italic>P</i>\u0000<sub>Peak</sub>\u0000, \u0000<italic>I</i>\u0000<sub>SC</sub>\u0000, \u0000<italic>V</i>\u0000<sub>OC</sub>\u0000, and fill factor, and identify the degradation modes that affected these modules. The modules under investigation were degraded under two different conditions: 16 modules were operated in the field, for 38 years, as a part of an off-grid photovoltaic system that was installed on the roof of the Faculty of Science, Sana'a University in Yemen, and one module was stored in a warehouse for the same period (exposure period<48>I</i>\u0000–\u0000<italic>V</i>\u0000 curve measurement, infrared thermal imaging, electroluminescence imaging, and insulation test have been carried out for each module. Upon comparing with reference values as given by the manufacturer, the median peak power degradation rate of the field-exposed modules over the outdoor exposure period was found to be 25.37%. While the peak power degradation rate of the warehoused module was found to be 14.30%. Encapsulant delamination from cells along grid fingers, corrosion of cell fingers, and hot spots were detected in the warehoused module. On the other hand, encapsulant discoloration, shunting defect, humidity corrosion, front delamination, cell fingers corrosion, interconnect ribbons corrosion, hot spots, and finger interruptions were the principal causes of performance degradation of the field-exposed modules. A description of the detected degradation modes includes a brief discussion of the limitations and benefits of the design of these modules.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 1","pages":"137-145"},"PeriodicalIF":2.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880324","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}
With the rapid expansion of large-scale photovoltaic (PV) plants, it is paramount for solar stakeholders to understand the reliability and efficiency of their plants to inform maintenance decisions, increase production, and understand the design factors that impact performance. Diagnosing underperformance in PV plants is challenging due to the relatively few monitoring points with respect to the large geographic footprint of the plant. This study introduces a cutting-edge method that transforms the analysis and management of key factors influencing PV plant performance, including performance loss rate, recoverable soiling, and major system changes. Identifying these factors is critical for deriving actionable insights. Leveraging advanced analytical techniques, such as wavelet transformation, robust regression, and extreme point analysis, this approach provides a nuanced understanding of these factors. This method has been tested across two synthetic datasets and one real dataset, consistently surpassing existing benchmarks by achieving a lower median mean absolute error and reduced error variability across all comparable components.
{"title":"Advanced Signal Decomposition Analysis and Anomaly Detection in Photovoltaic Systems","authors":"Mahya Qorbani;Daniel Fregosi;Devin Widrick;Kamran Paynabar","doi":"10.1109/JPHOTOV.2024.3483258","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2024.3483258","url":null,"abstract":"With the rapid expansion of large-scale photovoltaic (PV) plants, it is paramount for solar stakeholders to understand the reliability and efficiency of their plants to inform maintenance decisions, increase production, and understand the design factors that impact performance. Diagnosing underperformance in PV plants is challenging due to the relatively few monitoring points with respect to the large geographic footprint of the plant. This study introduces a cutting-edge method that transforms the analysis and management of key factors influencing PV plant performance, including performance loss rate, recoverable soiling, and major system changes. Identifying these factors is critical for deriving actionable insights. Leveraging advanced analytical techniques, such as wavelet transformation, robust regression, and extreme point analysis, this approach provides a nuanced understanding of these factors. This method has been tested across two synthetic datasets and one real dataset, consistently surpassing existing benchmarks by achieving a lower median mean absolute error and reduced error variability across all comparable components.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 1","pages":"155-163"},"PeriodicalIF":2.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880285","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}
Fabrication of bifacial translucent solar cell is a promising technology for the development of building integrated photovoltaics and the construction of tandem solar cell. In this work, cadmium telluride (CdTe) polycrystalline thin films with a thickness of 1 μ m were prepared by using close space sublimation system while nitrogen-doped zinc telluride (ZnTe: N) and tungsten-doped indium oxide (IWO) layers were deposited by using magnetron sputtering and reactive plasma deposition technology, respectively. After analyzing the optical and electrical properties of films and optimizing their deposition processes, a bifacial ultrathin solar cell with a 7.1% back illumination conversion efficiency was developed, which was currently the best back illumination efficiency for CdTe solar cell with an absorption layer thickness of no more than 1 μ m. Furthermore, the bifacial ultrathin CdTe solar cell with ZnTe:N/IWO composite transparent back electrode can achieved a maximum theoretical efficiency of up to 20% under the back illumination by employing SCAP software simulation design.
{"title":"Study on the Bifacial Ultrathin CdTe Solar Cell With ZnTe:N/IWO Composite Transparent Back Electrode","authors":"Xin Zhang;Xiutao Yang;Yujie Zheng;Yunpu Tai;Jingquan Zhang;Bing Li;Chebotareva Alla;Amangel'di Kamalov;Guanggen Zeng","doi":"10.1109/JPHOTOV.2024.3483249","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2024.3483249","url":null,"abstract":"Fabrication of bifacial translucent solar cell is a promising technology for the development of building integrated photovoltaics and the construction of tandem solar cell. In this work, cadmium telluride (CdTe) polycrystalline thin films with a thickness of 1 \u0000<italic>μ</i>\u0000m were prepared by using close space sublimation system while nitrogen-doped zinc telluride (ZnTe: N) and tungsten-doped indium oxide (IWO) layers were deposited by using magnetron sputtering and reactive plasma deposition technology, respectively. After analyzing the optical and electrical properties of films and optimizing their deposition processes, a bifacial ultrathin solar cell with a 7.1% back illumination conversion efficiency was developed, which was currently the best back illumination efficiency for CdTe solar cell with an absorption layer thickness of no more than 1 \u0000<italic>μ</i>\u0000m. Furthermore, the bifacial ultrathin CdTe solar cell with ZnTe:N/IWO composite transparent back electrode can achieved a maximum theoretical efficiency of up to 20% under the back illumination by employing SCAP software simulation design.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 1","pages":"40-45"},"PeriodicalIF":2.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880303","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 : 2024-10-28DOI: 10.1109/JPHOTOV.2024.3483263
Michael F. Miller;Alexandra M. Bothwell;Ana Kanevce;Stefan Paetel;Darius Kuciauskas;Aaron R. Arehart
Cu(In,Ga)Se 2 (CIGS) solar cells have benefited in recent years from the addition of heavy alkali elements, such as Rb, which increase the solar cell open-circuit voltage ( V OC ). To investigate the source of this improvement, here, we compare samples with and without Rb to perform a quantitative comparison of electronic defects and minority carrier lifetime. Deep-level transient and optical spectroscopy measurements were performed on two sets of rubidium fluoride (RbF)-treated and untreated CIGS, and three distinct traps were identified regardless of RbF treatment. The RbF treatment was found to reduce the concentration of the H2 trap, which was previously found to act as a recombination center and is located preferentially at CIGS grain boundaries. Time-resolved photoluminescence measurements showed an increase in effective lifetime after RbF and nearly all lifetime improvement resulted from reductions in bulk recombination. The observed V OC improvement is well correlated with increased minority carrier lifetime and acceptor concentration, which led to increases and decreases in electron and hole quasi-Fermi levels, respectively.
{"title":"Improved VOC in RbF-Treated Cu(In,Ga)Se2 Solar Cells via Passivation of Recombination Centers","authors":"Michael F. Miller;Alexandra M. Bothwell;Ana Kanevce;Stefan Paetel;Darius Kuciauskas;Aaron R. Arehart","doi":"10.1109/JPHOTOV.2024.3483263","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2024.3483263","url":null,"abstract":"Cu(In,Ga)Se\u0000<sub>2</sub>\u0000 (CIGS) solar cells have benefited in recent years from the addition of heavy alkali elements, such as Rb, which increase the solar cell open-circuit voltage (\u0000<italic>V</i>\u0000<sub>OC</sub>\u0000). To investigate the source of this improvement, here, we compare samples with and without Rb to perform a quantitative comparison of electronic defects and minority carrier lifetime. Deep-level transient and optical spectroscopy measurements were performed on two sets of rubidium fluoride (RbF)-treated and untreated CIGS, and three distinct traps were identified regardless of RbF treatment. The RbF treatment was found to reduce the concentration of the H2 trap, which was previously found to act as a recombination center and is located preferentially at CIGS grain boundaries. Time-resolved photoluminescence measurements showed an increase in effective lifetime after RbF and nearly all lifetime improvement resulted from reductions in bulk recombination. The observed \u0000<italic>V</i>\u0000<sub>OC</sub>\u0000 improvement is well correlated with increased minority carrier lifetime and acceptor concentration, which led to increases and decreases in electron and hole quasi-Fermi levels, respectively.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 1","pages":"67-72"},"PeriodicalIF":2.5,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880354","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 : 2024-10-28DOI: 10.1109/JPHOTOV.2024.3438451
Navid Tavakoli;Pascal Koelblin;Michael Saliba
Photovoltaic (PV) module performance is primarily characterized by their current-voltage ( I-V ) measurements. However, the data obtained mostly contains errors. Commercial I-V curve tracking units are generally expensive, hard to transport, slow to respond, and limited at low irradiations. This article proposes a novel I-V tracer (SEPIV) based on an optimized single-ended primary inductance converter. The SEPIV comprises a linear variable dc load connected to the solar panel's output. In this study, the experimental performance of the SEPIV was compared with the outcomes of commercial and lab devices, which are PVPM 1000 C 40 (PVPM) and WAVELABS SINUS-3000 PRO, respectively. SEPIV's accuracy matched lab units and surpassed PVPM's. As a highlight of this study, the introduced setup can capture the I-V curves of PV modules up to 650 W, a maximum V OC of 60 V, and a maximum I SC of 20 A. In contrast to commercial units, the SEPIV measurement does not depend on irradiation level. Moreover, it has the Internet of Things capability through a Wi-Fi connection for remote measurement.
{"title":"Design and Development of a New Smart Portable I-V Tracer","authors":"Navid Tavakoli;Pascal Koelblin;Michael Saliba","doi":"10.1109/JPHOTOV.2024.3438451","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2024.3438451","url":null,"abstract":"Photovoltaic (PV) module performance is primarily characterized by their current-voltage (\u0000<italic>I-V</i>\u0000) measurements. However, the data obtained mostly contains errors. Commercial \u0000<italic>I-V</i>\u0000 curve tracking units are generally expensive, hard to transport, slow to respond, and limited at low irradiations. This article proposes a novel \u0000<italic>I-V</i>\u0000 tracer (SEPIV) based on an optimized single-ended primary inductance converter. The SEPIV comprises a linear variable dc load connected to the solar panel's output. In this study, the experimental performance of the SEPIV was compared with the outcomes of commercial and lab devices, which are PVPM 1000 C 40 (PVPM) and WAVELABS SINUS-3000 PRO, respectively. SEPIV's accuracy matched lab units and surpassed PVPM's. As a highlight of this study, the introduced setup can capture the \u0000<italic>I-V</i>\u0000 curves of PV modules up to 650 W, a maximum \u0000<italic>V</i>\u0000<sub>OC</sub>\u0000 of 60 V, and a maximum \u0000<italic>I</i>\u0000<sub>SC</sub>\u0000 of 20 A. In contrast to commercial units, the SEPIV measurement does not depend on irradiation level. Moreover, it has the Internet of Things capability through a Wi-Fi connection for remote measurement.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"14 6","pages":"951-959"},"PeriodicalIF":2.5,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524163","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 : 2024-10-25DOI: 10.1109/JPHOTOV.2024.3472222
Edris Khorani;Sophie L. Pain;Tim Niewelt;Ruy S. Bonilla;Tasmiat Rahman;Nicholas E. Grant;John D. Murphy
Solar-based power generation presents challenges for system and grid operators due to the intermittent nature of power supply. Predicting the performance of photovoltaic (PV) power plants and rooftop systems can often be challenging due to difficulties in data collection and incoherencies in interconnected systems. Following the hierarchical aggregation structure from geographical and temporal similarities between PV systems, we suggest a simplified approach to predicting the performance of individual installations and evaluating the impact of these hypothetical installations on the overall grid. We use the hierarchical nature of power generation and ascertain weather datasets to predict the performance of new or existing systems for locations with unmeasured input data. We demonstrate an approach that could improve grid stability by using a hierarchical model on publicly available datasets on utility and rooftop installations. Ensemble machine learning algorithms are trained with 16 weeks of known hourly input training features to form a baseline model for known locations. The prediction accuracy is then directly compared for locations with known and unknown input features, both on a granular and subregion level. We observe a reduction in prediction accuracy by 6–8% using the hierarchical approach. The accuracy of the hierarchical model can be further enhanced beyond our work by increasing the training dataset temporally, as well as by augmenting nested layers of the hierarchy.
{"title":"Hierarchical Time-Series Approaches for Photovoltaic System Performance Forecasting With Sparse Datasets","authors":"Edris Khorani;Sophie L. Pain;Tim Niewelt;Ruy S. Bonilla;Tasmiat Rahman;Nicholas E. Grant;John D. Murphy","doi":"10.1109/JPHOTOV.2024.3472222","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2024.3472222","url":null,"abstract":"Solar-based power generation presents challenges for system and grid operators due to the intermittent nature of power supply. Predicting the performance of photovoltaic (PV) power plants and rooftop systems can often be challenging due to difficulties in data collection and incoherencies in interconnected systems. Following the hierarchical aggregation structure from geographical and temporal similarities between PV systems, we suggest a simplified approach to predicting the performance of individual installations and evaluating the impact of these hypothetical installations on the overall grid. We use the hierarchical nature of power generation and ascertain weather datasets to predict the performance of new or existing systems for locations with unmeasured input data. We demonstrate an approach that could improve grid stability by using a hierarchical model on publicly available datasets on utility and rooftop installations. Ensemble machine learning algorithms are trained with 16 weeks of known hourly input training features to form a baseline model for known locations. The prediction accuracy is then directly compared for locations with known and unknown input features, both on a granular and subregion level. We observe a reduction in prediction accuracy by 6–8% using the hierarchical approach. The accuracy of the hierarchical model can be further enhanced beyond our work by increasing the training dataset temporally, as well as by augmenting nested layers of the hierarchy.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 1","pages":"173-180"},"PeriodicalIF":2.5,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880357","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 : 2024-10-25DOI: 10.1109/JPHOTOV.2024.3480752
{"title":"IEEE Open Access Publishing","authors":"","doi":"10.1109/JPHOTOV.2024.3480752","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2024.3480752","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"14 6","pages":"975-975"},"PeriodicalIF":2.5,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10736228","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518125","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 : 2024-10-25DOI: 10.1109/JPHOTOV.2024.3480750
{"title":"TechRxiv: Share Your Preprint Research with the World!","authors":"","doi":"10.1109/JPHOTOV.2024.3480750","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2024.3480750","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"14 6","pages":"974-974"},"PeriodicalIF":2.5,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10736235","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517907","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 : 2024-10-25DOI: 10.1109/JPHOTOV.2024.3480754
{"title":"IEEE Women in Engine","authors":"","doi":"10.1109/JPHOTOV.2024.3480754","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2024.3480754","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"14 6","pages":"976-976"},"PeriodicalIF":2.5,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10736238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518126","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}
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