Discriminative common vector in sufficient data Case: A fault detection and classification application on photovoltaic arrays

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.