Renewable energy is found to be an effective alternative in the field of power production owing to the recent energy crises. Among the available renewable energy sources, solar energy is considered the front runner due to its ability to deliver clean energy, free availability and reduced cost. Photovoltaic (PV) modules are placed over large geographical regions for efficient solar energy harvesting, making it difficult to carry out maintenance and restoration works. Thermal stresses inherited by photovoltaic modules (PVM) under varying environmental conditions can lead to failure of internal components. Such failures when left undetected impart a number of complications in the system that will lead to unsafe operation and seizure. To avoid the aforementioned uncertainties, frequent monitoring of PVM is found necessary. The fault identification in PVM using essential features taken from aerial images is presented in this study. The feature extraction procedure was carried out using convolutional neural networks (CNN), while the feature selection process was carried out by the J48 decision tree method. Six test conditions were considered such as delamination, glass breakage, discoloration, burn marks, snail trail, and good panel. Bayes Net (BN) and Naïve Bayes (NB) classifiers were utilized as primary classifiers for all the test conditions. Results obtained from the classifiers were compared and the best classifier for fault detection in PVM is suggested.

中文翻译：

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利用深度学习特征检测光伏组件视觉故障的贝叶斯分类器的比较研究

由于最近的能源危机，可再生能源被认为是电力生产领域的有效替代方案。在可用的可再生能源中，太阳能因其提供清洁能源、免费使用和降低成本的能力而被认为是领先者。光伏（PV）模块被放置在大片地理区域以高效收集太阳能，这使得维护和恢复工作变得困难。光伏模块 (PVM) 在不同的环境条件下继承的热应力可能会导致内部组件发生故障。如果未检测到此类故障，则会给系统带来许多并发症，从而导致不安全操作和卡死。为了避免上述不确定性，有必要频繁监测 PVM。本研究提出了使用航空图像的基本特征进行 PVM 故障识别。特征提取过程是使用卷积神经网络（CNN）进行的，而特征选择过程是通过J48决策树方法进行的。考虑了六种测试条件，例如分层、玻璃破碎、变色、烧痕、蜗牛痕迹和良好面板。贝叶斯网络（BN）和朴素贝叶斯（NB）分类器被用作所有测试条件的主要分类器。对分类器获得的结果进行了比较，并提出了 PVM 中故障检测的最佳分类器。