Abstract
Categorization of PV faults is an essential task for improving the efficiency and reliability of a photovoltaic (PV) system. Output characteristics of a solar (PV) system can be severely affected under various fault conditions including short circuit, module mismatch, open circuit, and multiple faults under shading conditions. Such PV faults can potentially be analyzed through the PV characteristic curve analysis using a multilayer neural network with a scaled conjugate gradient algorithm (SCG). This paper presents an extensive investigation for categorization, i.e., classification of the above-mentioned PV faults using the SCG algorithm. The major contribution of the presented research work is the categorization of PV faults in sixteen different classes considering polycrystalline and thin-film PV technologies with two different configurations, including SP and TCT. The fault classification is achieved with high accuracy of 99.6% and a fast-computational time of 0.08 sec. The results are validated through the plot of the Confusion Matrix and Region of Convergence (ROC) with their performance evaluation in MATLAB. The achieved accuracy and fast computational time prove the effectiveness of the multilayer neural network-based approach for classification of the PV faults to increase power output, efficiency, and lifespan of PV systems.