In this study, the derivation of the Discriminative Common Vector (DCV) approach which is first introduced for a face recognition task in the insufficient data case, for the sufficient data case is obtained and it is applied for a photovoltaic (PV) panel fault detection and classification. Two experimental studies are performed including two different fault configurations. In the first experimental study, as the faulty conditions open-circuit, short-circuit, and partial shading conditions are taken and healthy condition is taken as reference. Thus, a four-class fault detection and classification scheme is constructed. In the second experimental study, the serial resistance degradation fault is considered. This fault detection and classification scheme includes four classes that are healthy and three different serial resistance degradation. The data used in the experimental studies are formed to be 1x3 dimensional vectors which include the current, voltage, and power values obtained from the simulations in the PSIM program. In all two experimental studies for each class, a discriminative common vector (DCV) which represents the common properties of that class, thus, having a high discriminative ability is obtained. As a contribution to the literature, the derivation of DCVA which has high discrimination ability for sufficient data case, and usage of it for PV panels fault detection and classification is proposed for the first time in this study. The proposed method's performance is evaluated with the performance of PCA method that is recently used for the fault detection and classification problem in PV panel systems in the literature. In the first experimental study, the proposed method's performance (99%) is obtained significantly higher than the performance of the PCA method (95%). And in the second experimental study, while PCA can only detect the faulty condition but cannot classify the serial resistance degradation, the proposed method can both detect and classify with 99% accuracy the PV panel serial resistance degradation.

在本研究中，判别公共向量 (DCV) 方法的推导首先引入数据不足情况下的人脸识别任务，获得足够数据情况并将其应用于光伏 (PV) 面板故障检测和分类。进行了两项实验研究，包括两种不同的故障配置。在第一个实验研究中，故障工况取开路、短路和局部遮蔽工况，并以健康工况为参考。因此，构建了四类故障检测和分类方案。在第二个实验研究中，考虑了串联电阻退化故障。这种故障检测和分类方案包括四个正常的类别和三个不同的串联电阻退化。实验研究中使用的数据形成为 1x3 维向量，其中包括从 PSIM 程序中的模拟中获得的电流、电压和功率值。在每个类别的所有两个实验研究中，都获得了代表该类别的共同属性的判别公共向量（DCV），因此，具有较高的判别能力。作为对文献的贡献，本研究首次提出了对足够数据案例具有高辨别能力的DCVA的推导，并将其用于光伏电池板故障检测和分类。所提出的方法的性能通过 PCA 方法的性能进行评估，PCA 方法最近用于文献中光伏电池板系统的故障检测和分类问题。在第一个实验研究中，所提出方法的性能（99%）明显高于PCA方法的性能（95%）。在第二个实验研究中，虽然 PCA 只能检测故障条件而不能对串联电阻退化进行分类，但所提出的方法可以以 99% 的准确率检测和分类光伏电池板串联电阻退化。