Fault diagnosis plays a significant role in enhancing the useful lifetime, power output, and reliability of photovoltaic modules (PVM). Visual faults such as burn marks, delamination, discoloration, glass breakage, and snail trails make detection of faults difficult under harsh environmental conditions. Various researchers have made several attempts to identify visual faults in a PVM. However, much of the previous studies were centered on the identification and analysis of limited number of faults. This article presents the use of a deep convolutional neural network (CNN) to extract image features and perform an effective classification of faults by machine learning (ML) algorithms. In contrast to the present-day work, five different fault conditions were considered in the study. The proposed solution consists of three phases, to effectively analyze various PVM defects. First, the module images are acquired using unmanned aerial vehicles (UAVs) and data augmentation is performed to generate a uniform dataset. Afterward, a pre-trained deep CNN is adopted for image feature extraction. Finally, the extracted image features are classified with the help of various ML classifiers. The final results show the effectiveness of pre-trained deep CNN and accurate performance of ML classifiers. The best-in-class ML classifier for multiple fault classification is suggested based on the performance comparison.

故障诊断在延长光伏模块（PVM）的使用寿命，功率输出和可靠性方面起着重要作用。诸如烧伤痕迹，分层，变色，玻璃破碎和蜗牛痕迹之类的视觉故障使得在恶劣的环境条件下难以检测故障。各种研究人员已经进行了几次尝试来识别PVM中的视觉故障。但是，以前的许多研究都集中在有限数量的故障的识别和分析上。本文介绍了使用深度卷积神经网络（CNN）提取图像特征并通过机器学习（ML）算法对故障进行有效的分类。与目前的工作相反，研究中考虑了五种不同的断层条件。提议的解决方案包括三个阶段，有效分析各种PVM缺陷。首先，使用无人飞行器（UAV）采集模块图像，并进行数据扩充以生成统一的数据集。之后，采用预训练的深度CNN进行图像特征提取。最后，借助各种ML分类器对提取的图像特征进行分类。最终结果显示了预训练的深度CNN的有效性以及ML分类器的准确性能。基于性能比较，提出了用于多故障分类的同类最佳机器学习分类器。借助各种ML分类器对提取的图像特征进行分类。最终结果显示了预训练的深度CNN的有效性以及ML分类器的准确性能。基于性能比较，提出了用于多故障分类的同类最佳机器学习分类器。借助各种ML分类器对提取的图像特征进行分类。最终结果显示了预训练的深度CNN的有效性和ML分类器的准确性能。基于性能比较，提出了用于多故障分类的同类最佳机器学习分类器。