A combined approach of convolutional neural networks and machine learning for visual fault classification in photovoltaic modules. (February 2022)

• Record Type: Journal Article
• Authors: Venkatesh, Sridharan Naveen; Sugumaran, Vaithiyanathan
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 236:Number 1(2022)
• Page Start: 148
• Page End: 159
• Publication Date: 2022-02
• Subjects: Visual faults -- fault diagnosis -- machine learning -- convolutional neural networks -- feature extraction -- photovoltaic modules; Reliability (Engineering) -- Mathematical models -- Periodiclals; Risk assessment -- Mathematical models -- Periodicals; Engineering design -- Mathematical models -- Periodicals; 620.00452
• DOI: 10.1177/1748006X211020305
• Languages: English
• ISSNs: 1748-006X

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.