Han Dongwoon, Kim Seongtak
Gangwon Technology Application Division, Korea Institute of Industrial Technology, Wonju, 26336, Republic of Korea.
Sci Rep. 2024 Jun 25;14(1):14592. doi: 10.1038/s41598-024-64594-4.
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 , and the average mean absolute percentage error (MAPE) and coefficient of determination ( ) 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.
最近，太阳能电池中使用的硅片尺寸不断增大，导致组件尺寸和重量增加。重量增加会导致光伏（PV）组件发生挠曲，这可能会导致电池效率降低。在本研究中，我们开发了一种基于深度神经网络（DNN）的有限元（FE）替代模型，以获得可改善大规模双面光伏组件挠曲的最佳框架设计因素。首先，为大规模双面光伏组件构建了一个有限元模型。在此基础上，使用在所提出的因素范围内生成的243个有限元分析（FEA）数据集对有限元替代模型进行训练。此外，通过贝叶斯优化和k折验证对其进行了改进。结果，最终损失值为 ，训练集挠曲和重量的平均平均绝对百分比误差（MAPE）和决定系数（ ）值分别为0.0017、0.9972，测试集分别为0.0020、0.9962。这表明训练后的有限元替代模型具有很高的准确性。在框架设计因素范围内生成100万个数据集后，使用训练后的模型进行预测。基于这些数据，将使挠曲和重量最小化的框架设计因素确定为约a = 1.5、b = 13.7、c = 1.5、d = 3.0、e = 4.3。此时，挠曲为11.1毫米，重量为3.6千克。使用导出的因素改变框架形状后，进行了有限元分析。挠曲和重量的结果匹配，几乎没有误差。此时，重量比现有重量增加了约12.8%，而挠曲减少了约9.6%。此外，我们分析了每个因素的挠曲与重量之间的关系，并为导出的结果奠定了基础。因此，我们的有限元替代模型准确地预测了有限元分析结果，并快速确定了使挠曲和重量最小化的最佳因素。
