Accurately detecting faults in photovoltaic modules/cells and estimating their effective power output and parameters of the equivalent circuit representation of photovoltaic modules is becoming increasingly critical for both the reliability of associated systems and the efficiency of electricity production from renewable energy sources. Existing studies often work with datasets containing photovoltaic cells that exhibit one fault at a time, leading to the classification of photovoltaic cells with multiple faults as “mixed” faults. Moreover, factors such as cell alignment and specific fault types, collectively called “cell level features”, are not considered in current studies estimating the power output of a photovoltaic module. Therefore, this paper focuses on a comprehensive deep-learning pipeline to separately detect three types of faults in photovoltaic modules/cells using electroluminescence images. Furthermore, it addresses the estimation of the output power of photovoltaic modules and the series resistance of their equivalent circuit, considering the cell-level characteristics extracted from the electroluminescence images. The proposed model demonstrates its ability to detect “black core”, “crack”, and “edge” faults with global accuracies of 0.93, 0.868, and 0.95, respectively. Furthermore, the proposed model estimates the power output of photovoltaic modules with a normalized mean absolute error of 0.03547 and a normalized root mean squared error of 0.04892. This outperforms the base model that relies solely on non-pre-processed detected faults and significantly larger models adept at extracting features from the electroluminescence images. Moreover, the VGG16-based model estimates the series resistance in the equivalent circuit representation of photovoltaic modules with a normalized mean absolute error of 0.04472 and a normalized root mean squared error of 0.0622.

中文翻译：

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光伏组件故障检测、功率输出和参数估计：基于电致发光图像的深度学习方法


准确检测光伏模块/电池中的故障并估计其有效功率输出和光伏模块等效电路表示的参数对于相关系统的可靠性和可再生能源发电的效率变得越来越重要。现有的研究通常使用包含一次出现一个故障的光伏电池的数据集，从而将具有多个故障的光伏电池分类为“混合”故障。此外，当前估计光伏组件功率输出的研究并未考虑诸如电池排列和特定故障类型等统称为“电池级特征”的因素。因此，本文重点研究综合深度学习管道，利用电致发光图像分别检测光伏组件/电池中的三种类型的故障。此外，考虑到从电致发光图像中提取的电池级特性，它还可以估计光伏模块的输出功率及其等效电路的串联电阻。所提出的模型展示了其检测“黑芯”、“裂纹”和“边缘”故障的能力，全局精度分别为 0.93、0.868 和 0.95。此外，所提出的模型估计了光伏组件的功率输出，归一化平均绝对误差为0.03547，归一化均方根误差为0.04892。这优于仅依赖于未预处理的检测到的故障的基本模型以及擅长从电致发光图像中提取特征的明显更大的模型。 此外，基于VGG16的模型估计了光伏组件等效电路表示中的串联电阻，归一化平均绝对误差为0.04472，归一化均方根误差为0.0622。