A combined approach of convolutional neural networks and machine learning for visual fault classification in photovoltaic modules

Abstract

Fault diagnosis plays a significant role in enhancing the useful lifetime, power output, and reliability of photovoltaic modules (PVM). Visual faults such as burn marks, delamination, discoloration, glass breakage, and snail trails make detection of faults difficult under harsh environmental conditions. Various researchers have made several attempts to identify visual faults in a PVM. However, much of the previous studies were centered on the identification and analysis of limited number of faults. This article presents the use of a deep convolutional neural network (CNN) to extract image features and perform an effective classification of faults by machine learning (ML) algorithms. In contrast to the present-day work, five different fault conditions were considered in the study. The proposed solution consists of three phases, to effectively analyze various PVM defects. First, the module images are acquired using unmanned aerial vehicles (UAVs) and data augmentation is performed to generate a uniform dataset. Afterward, a pre-trained deep CNN is adopted for image feature extraction. Finally, the extracted image features are classified with the help of various ML classifiers. The final results show the effectiveness of pre-trained deep CNN and accurate performance of ML classifiers. The best-in-class ML classifier for multiple fault classification is suggested based on the performance comparison.

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

“…The faults detected in this case are mostly related to visible problems, such as delamination, burn marks and glass breakages. An important issue that makes the comparison between studies difficult is the difference between the data resolution used as input for each one of them, as images vary from PV cells [140] to aerial images [141]. [151] k-means, SVM and CNN MCC: 1.0 Detection of damaged modules on rooftops [141] Naïve Bayes, SVM, k-nearest neighbors, decision tree, RF and pretrained DL models Ac: 100% Detection of burn marks, delamination, discoloration, glass breakages and snail trails [152] DL Ac: 93% Masks of bird droppings Tables 6 and 7 present a summary of the methods for detecting and classifying faults in aIRT images of PV systems, using DIP and DL algorithms, respectively.…”

“…An important issue that makes the comparison between studies difficult is the difference between the data resolution used as input for each one of them, as images vary from PV cells [140] to aerial images [141]. [151] k-means, SVM and CNN MCC: 1.0 Detection of damaged modules on rooftops [141] Naïve Bayes, SVM, k-nearest neighbors, decision tree, RF and pretrained DL models Ac: 100% Detection of burn marks, delamination, discoloration, glass breakages and snail trails [152] DL Ac: 93% Masks of bird droppings Tables 6 and 7 present a summary of the methods for detecting and classifying faults in aIRT images of PV systems, using DIP and DL algorithms, respectively. In general, the algorithms with the highest results are the ones dedicated to the detection of faults or the classification of a few types of faults, with the classification of many classes of faults being a much more complex task.…”

A Review on Machine Learning Applications for Solar Plants

“…In studies [ 106 , 107 , 108 , 109 ], researchers localized and identified different failures of a solar plant system based on CNNs that process the solar panels’ images, including thermographic images [ 106 , 107 , 108 ]. In Table 10 , we summarize the ML technologies for PV diagnostics from studies [ 106 , 107 , 108 , 109 , 110 , 111 , 112 ].…”

Detection of visual faults in photovoltaic modules using a stacking ensemble approach

“…Reports reveal that renewable energy sources contribute about 27.3% of the total electricity production across the globe with PV-based power production accounting for about 2.8%. PV-based power production is placed next to wind energy power generation as one of the leading contributors to power generation [ 3 ]. PV-based power generation employs PV modules to convert the incident light into electricity.…”