Assessment of Machine and Deep Learning Approaches for Fault Diagnosis in Photovoltaic Systems Using Infrared Thermography

Abstract

Nowadays, millions of photovoltaic (PV) plants are installed around the world. Given the widespread use of PV supply systems and in order to keep these PV plants safe and to avoid power losses, they should be carefully protected, and eventual faults should be detected, classified and isolated. In this paper, different machine learning (ML) and deep learning (DL) techniques were assessed for fault detection and diagnosis of PV modules. First, a dataset of infrared thermography images of normal and failure PV modules was collected. Second, two sub-datasets were built from the original one: The first sub-dataset contained normal and faulty IRT images, while the second one comprised only faulty IRT images. The first sub-dataset was used to develop fault detection models referred to as binary classification, for which an image was classified as representing a faulty PV panel or a normal one. The second one was used to design fault diagnosis models, referred to as multi-classification, where four classes (Fault1, Fault2, Fault3 and Fault4) were examined. The investigated faults were, respectively, failure bypass diode, shading effect, short-circuited PV module and soil accumulated on the PV module. To evaluate the efficiency of the investigated models, convolution matrix including precision, recall, F1-score and accuracy were used. The results showed that the methods based on deep learning exhibited better accuracy for both binary and multiclass classification while solving the fault detection and diagnosis problem in PV modules/arrays. In fact, deep learning techniques were found to be efficient for the detection and classification of different kinds of defects with good accuracy (98.71%). Through a comparative study, it was confirmed that the DL-based approaches have outperformed those based on ML-based algorithms.