Evolution of cell cracks in <scp>PV</scp>‐modules under field and laboratory conditions

Buerhop‐Lutz, Claudia; Wirsching, Sven; Bemm, Andreas; Pickel, Tobias; Hohmann, Philipp; Nieß, M.; Vodermayer, C.; Huber, Alexander; Glück, B.; Mergheim, Julia; Camus, Christian; Hauch, Jens; Brabec, Christoph J.

doi:10.1002/pip.2975

Cited by 48 publications

(21 citation statements)

References 15 publications

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“…UV humid in Table 2) in this work, which implies the consistency of this work with the observed surface crack after 5 to 6 years field exposure. 5,40,48 The root cause of surface crack initiation and propagation is therefore closely tied to material degradation.…”

Section: Analysis and Comparison With Outdoor Field Test Resultsmentioning

confidence: 99%

A novel test method for quantifying cracking propensity of photovoltaic backsheets after ultraviolet exposure

Lin

Jacobs

et al. 2018

Progress in Photovoltaics

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Surface cracking in multilayered backsheets is one of the common failure modes for field photovoltaic (PV) modules, leading to safety concerns. However, the current IEC qualification tests such as IEC 61215 cannot adequately predict the backsheet cracking in fielded modules. Moreover, little is known about effect of key environmental factors on the cracking behaviors of backsheets. In this study, a novel test method combining accelerated weathering with in situ surface cracking monitoring during tensile deformation is developed to quantify cracking propensity of backsheets after aging. A polyester‐based backsheet was selected, and 4 environmental conditions (UV/85°C/5% relative humidity (RH), UV/85°C/60% RH, 85°C/5% RH, 85°C/60% RH) were used for backsheet aging. The relationship between material degradation and crack formation of the backsheet under different environmental conditions is discussed. The channel cracks (fragmentation) perpendicular to the loading direction were only observed on the UV‐aged samples, not on those unaged or aged without UV irradiation. Moisture played a synergistic role with UV in the formation of surface cracking. Mode I fracture toughness (KIC) of the embrittled surface layer obtained from this test dropped by 98% relative to fresh samples, indicating a much higher cracking propensity of this backsheet after UV exposure. The proposed method was further validated by fragmentation tests of outdoor‐exposed backsheet samples. This method can serve as a quantitative tool to evaluate the cracking propensity of backsheets for a better material selection and lifetime prediction of PV modules.

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Section: Analysis and Comparison With Outdoor Field Test Resultsmentioning

confidence: 99%

A novel test method for quantifying cracking propensity of photovoltaic backsheets after ultraviolet exposure

Lin

Jacobs

et al. 2018

Progress in Photovoltaics

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“…These cracks may or may not lead to power reductions because the power output of a PV module can be affected differently depending on whether cracked cells are located on the same string or on different strings [20]. Buerhop et al [21] defined a "damage factor D" to quantify the degree of cracked damage of the PV modules and thus to gain a better understanding of the degradation of pre-cracked PV modules. Based on their classification criteria, the damage factor D can be classified into six classes to determine the weighting factors W according to the severity and the average of the cell damage factors.…”

Section: Description Of Cracked and Non-cracked Bipv Modulesmentioning

confidence: 99%

The Impact of Cracks in BIPV Modules on Power Outputs: A Case Study Based on Measured and Simulated Data

Lee

et al. 2021

Energies

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Crack issues afflicting a building integrated photovoltaics (BIPV) system are major concerns in terms of the system’s maintenance and power degradation. Although there may be many circumstances that bring about cracks in BIPV modules during the installation process, identifying the degradation of PV module efficiency resulting from the effects of cracks tends to be a very difficult task unless actual indoor or outdoor tests or detailed electroluminescence imaging tests are conducted. Many current studies have demonstrated that cracks may or may not impact the output performance of PV modules depending on the damage levels or where the damage is located. For BIPV applications such as replacement for building materials, there is still a lack of information and case studies addressing crack issues in a quantitative manner for evaluating BIPV output performance. Therefore, the objectives of this study are to investigate the effects of cracks in BIPV modules on power outputs and to identify detailed relationships between the cracks and power output based on experimental and simulated analysis. An experimental facility located in Daejeon, South Korea, was used to gather data from cracked and non-cracked BIPV modules. By using the field-measured data and facility’ information, a simulation model was developed using SolarPro software, and a simulated-based analysis was conducted to evaluate the impact of cracks in BIPV modules on output values after proper validation of the model. The results from this study reveal that cracks in BIPV modules exhibit significant degradation in BIPV modules’ outputs of up to 43% reduction during the experimental period. From the annual comparative results, output degradations of 34.6–35.4% were estimated when the BIPV modules included cracks. As a result, the cracks in the BIPV modules could be carefully addressed as issues occurring in the BIPV installation process.

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“…This is because many defect types can be identified easily and, as opposed to infrared (IR) measurements, a quantification of the active area of a cell is possible. 1 The latter makes it especially well suited for automated prediction of the module power by statistical methods like deep learning (DL). Traditionally, the maximum power point (MPP) is determined from direct measurements of the IV curve.…”

Section: Introductionmentioning

confidence: 99%

Deep‐learning‐based pipeline for module power prediction from electroluminescense measurements

Hoffmann¹,

Buerhop‐Lutz

Reeb

et al. 2021

Progress in Photovoltaics

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Automated inspection plays an important role in monitoring large-scale photovoltaic power plants. Commonly, electroluminescense measurements are used to identify various types of defects on solar modules, but have not been used to determine the power of a module. However, knowledge of the power at maximum power point is important as well, since drops in the power of a single module can affect the performance of an entire string. By now, this is commonly determined by measurements that require to discontact or even dismount the module, rendering a regular inspection of individual modules infeasible. In this work, we bridge the gap between electroluminescense measurements and the power determination of a module. We compile a large dataset of 719 electroluminescense measurements of modules at various stages of degradation, especially cell cracks and fractures, and the corresponding power at maximum power point. Here, we focus on inactive regions and cracks as the predominant type of defect. We set up a baseline regression model to predict the power from electroluminescense measurements with a mean absolute error (MAE) of 9.0 ± 8.4W P (4.0 ± 3.7%). Then, we show that deep learning can be used to train a model that performs significantly better (7.3 ± 6.5W P or 3.2 ± 2.7%) and propose a variant of class activation maps to obtain the per cell power loss, as predicted by the model. With this work, we aim to open a new research topic. Therefore, we publicly release the dataset, the code, and trained models to empower other researchers to compare against our results. Finally, we present a thorough evaluation of certain boundary conditions like the dataset size and an automated preprocessing pipeline for on-site measurements showing multiple modules at once.

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Evolution of cell cracks in PV‐modules under field and laboratory conditions

Cited by 48 publications

References 15 publications

A novel test method for quantifying cracking propensity of photovoltaic backsheets after ultraviolet exposure

A novel test method for quantifying cracking propensity of photovoltaic backsheets after ultraviolet exposure

The Impact of Cracks in BIPV Modules on Power Outputs: A Case Study Based on Measured and Simulated Data

Deep‐learning‐based pipeline for module power prediction from electroluminescense measurements

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