An efficient approach for diagnosing faults in photovoltaic array using 1D-CNN and feature selection Techniques

Abstract

• A deep learning method based on the 1D-CNN model is proposed to diagnose the faults for PV Arrays. • Feature permutation has been applied to select the most relevant features based on their importance. • The training datasets are created for five types of faults, including PSC, LLF, OCF, AF, and DF. • The proposed method efficiently diagnoses the faults in Series -Parallel, Total Cross-Tied, and Series PV arrays. Diagnosing faults in Photovoltaic (PV) systems is essential for operation and maintenance. Selecting relevant features is necessary for successful fault diagnosis because redundant and irrelevant features reduce fault diagnosing accuracy. This paper proposes a novel and efficient approach to diagnosing faults in PV systems. The Feature Selection and Fault Diagnosis (FSFD) method is executed for diagnosing five types of faults in PV array (PVA): partial shading condition, line-line fault, arc fault, open-circuit fault, and degradation fault. Firstly, a PVA modeling method using MATLAB/Simulink is employed to simulate I-V curves and extract their features. Next, a feature permutation technique-based method is proposed for selecting the most relevant features. A simple and accurate one-dimensional convolutional neural network (1D-CNN) model is developed to classify the faults based on the selected features. Finally, a confusion matrix is utilized to evaluate the performance of the trained model. Three datasets of PVAs have been utilized to evaluate the effectiveness of the proposed FSFD method. The results indicate that the FSFD method has effectively identified the best five features out of eight for training the 1D-CNN model. The trained model has achieved diagnosing accuracy rates of 99.85%, 99.73%, and 99.97% in series–parallel PVA, total cross-tied PVA, and series PVA datasets, respectively. The proposed method accurately diagnoses single faults in three PVA configurations. Therefore, we recommend conducting additional studies to improve the proposed method for diagnosing hybrid faults.

Topics & Concepts