Influence of Photovoltaic Module Mounting Systems on the Thermo-Mechanical Stresses in Solar Cells by FEM Modelling

Aktaa, Jarir; Eitner, Ulrich; Ebert, Matthieu; Beinert, Andreas J.

doi:10.4229/eupvsec20162016-5bv.1.14

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Figure 1a shows the FEA results of the large-scale bifacial PV module with a commercially available Al frame. The analysis revealed that the maximum deflection occurred at the center of the module, confirming that this behavior is consistent with previous studies on the mechanical loading of PV module 2,5,6,12,13 . At this point, the maximum deflection of PV module was 12.3 mm, and the weight of frame was 3.2 kg, with a displacement of up to approximately 2.8 mm in the opposite direction occurring due to the reaction force caused by deflection from the support point to the end of the module.…”

Section: Resultssupporting

confidence: 91%

Design optimization of large-scale bifacial photovoltaic module frame using deep learning surrogate model

Han,

Kim

2024

Sci Rep

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Recently, the wafers used in solar cells have been increasing in size, leading to larger module sizes and weights. The increased weight can cause deflection of photovoltaic (PV) module, which may lead to decreased cell efficiency. In this study, we developed a deep neural network (DNN)-based finite element (FE) surrogate model to obtain the optimal frame design factors that can improve deflection in large-scale bifacial PV module. Initially, an FE model was constructed for large-scale bifacial PV module. Based on this, the FE surrogate model was trained using 243 FEA datasets generated within the proposed range of factors. Furthermore, it was improved through Bayesian optimization and k-fold validation. As a result, the final loss value was $$3.743 \times 10^{-4}$$ 3.743 × 10 - 4 , and the average mean absolute percentage error (MAPE) and coefficient of determination ($$R^2$$ R 2 ) values for deflection and weight were 0.0017, 0.9972 for the training set, and 0.0020, 0.9962 for the test set, respectively. This indicates that the trained FE surrogate model possesses significant accuracy. After generating 1 million datasets within the range of frame design factors, the trained model was used to obtain predictions. Based on this data, the frame design factors that minimize both deflection and weight were identified as about a = 1.5, b = 13.7, c = 1.5, d = 3.0, e = 4.3. At this point, the deflection was 11.1 mm, and the weight was 3.6 kg. After altering the frame shape with the derived factors, FEA was conducted. The results matched for both deflection and weight, with almost no error. At this point, the weight increased by approximately 12.8% compared to the existing, while the deflection decreased by about 9.6%. Additionally, we analyzed the relationship between deflection and weight for each factor and secured the basis for the derived results. Consequently, our FE surrogate model accurately predicted the FEA results and quickly identified the optimal factors that minimize deflection and weight.

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Section: Resultssupporting

confidence: 91%

Design optimization of large-scale bifacial photovoltaic module frame using deep learning surrogate model

Han,

Kim

2024

Sci Rep

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“…The primary methodologies employed in the literature for evaluating the impacts of mechanical loads on PV modules are FEM methods alongside outdoor and indoor assessments. It is worth mentioning that PV mounting system and its associated boundary conditions could have considerable effects on the stresses within PV module [39], where frames can make a significant contribution in reducing mechanical stresses [40]. Overall, the literature concentrated on the effects of mechanical loads on silicon-based solar cells and PV laminates, not PV aluminum (Al) frame, which will be discussed subsequently.…”

Section: Literature Backgroundmentioning

confidence: 99%

“…Deflections and stresses of the module are also highly dependent on the stiffness behavior of ethylene vinyl acetate (EVA) used for encapsulation. Beinert et al [40] furthered this research by comparing the clamping of framed and unframed standard glass-backsheet PV modules under 2,400 Pa and 5,400 Pa using FEA. They considered the thermal stress from the lamination process in their analysis as well.…”

Section: Effect Of Boundary Conditions and The Mounting System On The...mentioning

confidence: 99%

Mechanical and economic analysis of conventional aluminum photovoltaic module frames, frames with side holes, and open-source downward-fastened frames for non-traditional racking

Sadat¹,

Vandewetering²,

Pearce³

2022

Preprint

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Using bolts through the back of a solar photovoltaic (PV) module frames to attach them to racking is time consuming and awkward, so commercial PV installations use clamping technologies on the front. Conventional and proprietary clamps are costly and demand access to supply chains for uncommon mechanical components that limits deployment velocity. To overcome these challenges, this study presents new open-source downward-fastened and side-fastened aluminum (Al) framing designs, which are easy to install and compatible with metal and wood racks. The proposed parametric open source designs are analyzed through finite element methods (FEM) simulations and economic analysis is performed to compare to conventional PV frame at both the module and system levels. The FEM results showed all the frames have acceptable mechanical reliability and stability to pass IEC 61215 standards. The results show the new frame (with bottom width of 29 mm and thickness of 1.5 mm) have about a 2% land use efficiency penalty, but have better mechanical stability (lower stress and deflections), are easier to install and have reduced material economic costs (2%) compared to conventional frames. The results are promising for the use of the new PV frame designs for distributed manufacturing targeted at specific applications.

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“…Thickness of components in large-scale bifacial photovoltaic module GPa, C 12 = 63.5 GPa, C 44 = 79.0 GPa이다[15][16][17][18] . Mechanical properties of components in large-scale bifacial photovoltaic module…”

mentioning

confidence: 99%

Design Optimization of Large-scale Bifacial Photovoltaic Module Frame Based on FEM and RSM

Han,

Kim

2023

J Korean Solar Energy

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Large-scale bifacial photovoltaic modules have the advantages of high unit yield and production. Because the module weight increases with the module size, the power conversion efficiency can decrease owing to defects such as encapsulant delamination and cell cracking caused by the increased deflection of the module. However, research on the design optimization of large-scale bifacial photovoltaic module frames is lacking. Therefore, in this study, we analyzed the deflection of large-scale bifacial photovoltaic modules using finite element analysis (FEA). Furthermore, we propose an optimal design for a solar module frame that minimizes both module deflection and frame weight using the response surface methodology (RSM). We established 32 experimental setups utilizing the design of experiments with five frame shape factors placed at two levels each. The results of structural analysis showed that the maximum deflection occurred at the center of the module. The results obtained using RSM for minimizing deflection and weight yielded optimal factors as follows: a, b, c, d, e were found to be 1.9 mm, 14.0 mm, 1.5 mm, 1.7 mm, and 2.5 mm, respectively. A comparison of the deflection and weight between the existing and optimal conditions obtained from structural analysis revealed an increase in weight of approximately 4.5% compared to the existing conditions. Simultaneously, a deflection reduction of approximately 15.8% was observed.

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Influence of Photovoltaic Module Mounting Systems on the Thermo-Mechanical Stresses in Solar Cells by FEM Modelling

Cited by 5 publications

References 9 publications

Design optimization of large-scale bifacial photovoltaic module frame using deep learning surrogate model

Design optimization of large-scale bifacial photovoltaic module frame using deep learning surrogate model

Mechanical and economic analysis of conventional aluminum photovoltaic module frames, frames with side holes, and open-source downward-fastened frames for non-traditional racking

Design Optimization of Large-scale Bifacial Photovoltaic Module Frame Based on FEM and RSM

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