PV Cell Cracks and Impacts on Electrical Performance

Whitaker, Carolina M.; Pierce, Benjamin G.; Karimi, Ahmad Maroof; French, Roger H.; Braid, Jennifer L.

doi:10.1109/pvsc45281.2020.9300374

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…Furthermore, cracks and power loss in modules has been evaluated using light and dark I-V measurements over several stages of degradation [19]. Characterization of electrical parameters and statistical analysis of cracked PERC and Al-BSF cells in mini-modules was analyzed by Whitaker [20] and very recently, deep learning has been used to make power predictions from EL images of modules with solar cell cracks [21].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Electrical Parameters of Cracked Crystalline Silicon Solar Cells in Photovoltaic Modules

Santamaria

Benatto

Lancia

et al. 2021

2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)

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In this work we investigate the characteristics of solar cells cracks in photovoltaic (PV) modules for understanding the extent to which the solar cell electrical parameters change due to cell crack degradation. The experimental investigation is performed on two custom nine-cell mini-modules of mono-and multi-crystalline silicon, respectively, where each solar cell in the module has a junction box, allowing individual and module level characterization. Results show that power loss caused by cell cracks is driven primarily by a reduction of the cell's maximum power point current, in particular B type cracks. C cracks also affect the short circuit current of the cells, whereas cells with combined B and C cracks show the most reduction of the short circuit current. Equivalent circuit diode model curve fitting and analysis of the light or dark I-V curves proved of limited used in analyzing degradation of cracked cells, as the model assumptions break down. However, a comparative analysis of dark and light I-V curves with a Suns-Voc curve was better suited for understanding the evolution of diode parameters on cracked cells for increasing levels of degradation.

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Section: Introductionmentioning

confidence: 99%

Characterization of Electrical Parameters of Cracked Crystalline Silicon Solar Cells in Photovoltaic Modules

Santamaria

Benatto

Lancia

et al. 2021

2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)

View full text Add to dashboard Cite

show abstract

“…The authors in [65] used ESL Solar 500 tracer for measuring the I-V curves of degraded modules in ambient conditions with different irradiance and temperature. In a similar study in [114], Whitaker et al used a Spire 4600 SLP Flash Tester as well as I-V curve tracer at 1 sun illumination to measure I-V parameters in a degradation analysis of cell cracks. Parameters measured include time-series maximum power, voltage, and current, etc.…”

Section: Electrical Parameter Measurementmentioning

confidence: 99%

Solar Photovoltaic Modules’ Performance Reliability and Degradation Analysis—A Review

2022

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The current geometric increase in the global deployment of solar photovoltaic (PV) modules, both at utility-scale and residential roof-top systems, is majorly attributed to its affordability, scalability, long-term warranty and, most importantly, the continuous reduction in the levelized cost of electricity (LCOE) of solar PV in numerous countries. In addition, PV deployment is expected to continue this growth trend as energy portfolio globally shifts towards cleaner energy technologies. However, irrespective of the PV module type/material and component technology, the modules are exposed to a wide range of environmental conditions during outdoor deployment. Oftentimes, these environmental conditions are extreme for the modules and subject them to harsh chemical, photo-chemical and thermo-mechanical stress. Asides from manufacturing defects, these conditions contribute immensely to PV module’s aging rate, defects and degradation. Therefore, in recent times, there has been various investigations into PV reliability and degradation mechanisms. These studies do not only provide insight on how PV module’s performance degrades over time, but more importantly, they serve as meaningful input information for future developments in PV technologies, as well as performance prediction for better financial modelling. In view of this, prompt and efficient detection and classification of degradation modes and mechanisms due to manufacturing imperfections and field conditions are of great importance towards minimizing potential failure and associated risks. In the literature, several methods, ranging from visual inspection, electrical parameter measurements (EPM), imaging methods, and most recently data-driven techniques have been proposed and utilized to measure or characterize PV module degradation signatures and mechanisms/pathways. In this paper, we present a critical review of recent studies whereby solar PV systems performance reliability and degradation were analyzed. The aim is to make cogent contributions to the state-of-the-art, identify various critical issues and propose thoughtful ideas for future studies particularly in the area of data-driven analytics. In contrast with statistical and visual inspection approaches that tend to be time consuming and require huge human expertise, data-driven analytic methods including machine learning (ML) and deep learning (DL) models have impressive computational capacities to process voluminous data, with vast features, with reduced computation time. Thus, they can be deployed for assessing module performance in laboratories, manufacturing, and field deployments. With the huge size of PV modules’ installations especially in utility scale systems, coupled with the voluminous datasets generated in terms of EPM and imaging data features, ML and DL can learn irregular patterns and make conclusions in the prediction, diagnosis and classification of PV degradation signatures, with reduced computation time. Analysis and comparison of different models proposed for solar PV degradation are critically reviewed, in terms of the methodologies, characterization techniques, datasets, feature extraction mechanisms, accelerated testing procedures and classification procedures. Finally, we briefly highlight research gaps and summarize some recommendations for the future studies.

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Defect detection for PV Modules based on the improved YOLOv5s

Qian

Shen

et al. 2022

2022 China Automation Congress (CAC)

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PV Cell Cracks and Impacts on Electrical Performance

Cited by 7 publications

References 3 publications

Characterization of Electrical Parameters of Cracked Crystalline Silicon Solar Cells in Photovoltaic Modules

Characterization of Electrical Parameters of Cracked Crystalline Silicon Solar Cells in Photovoltaic Modules

Solar Photovoltaic Modules’ Performance Reliability and Degradation Analysis—A Review

Defect detection for PV Modules based on the improved YOLOv5s

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