A Novel Approach for Efficient Solar Panel Fault Classification Using Coupled UDenseNet

Abstract

Photovoltaic (PV) systems have immense potential to generate clean energy, and their adoption has grown significantly in recent years. A PV fault is a condition of a PV module that is unable to produce optimal power due to environmental factors, such as shading, hot spots, cracks, and other defects. The occurrence of faults in PV systems can present safety risks, shorten system lifespans, and result in waste. Therefore, this paper discusses the importance of accurately classifying faults in PV systems to maintain optimal operating efficiency, thereby increasing the financial return. Previous studies in this area have largely relied on deep learning models, such as transfer learning, with high computational requirements, which are limited by their inability to handle complex image features and unbalanced datasets. The proposed lightweight coupled UdenseNet model shows significant improvements for PV fault classification compared to previous studies, achieving an accuracy of 99.39%, 96.65%, and 95.72% for 2-class, 11-class, and 12-class output, respectively, while also demonstrating greater efficiency in terms of parameter counts, which is particularly important for real-time analysis of large-scale solar farms. Furthermore, geometric transformation and generative adversarial networks (GAN) image augmentation techniques improved the model’s performance on unbalanced datasets.

Radiometric Infrared Thermography of Solar Photovoltaic Systems: An Explainable Predictive Maintenance Approach for Remote Aerial Diagnostic Monitoring

“…Different approaches have been recently proposed in the context of detecting and/or classifying SPV panel defects, as can be seen from Table 1 [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57].…”

“…The tools and frameworks applied for the preprocessing and analysis include MATLAB/Simulink ® [43], Adobe Photoshop ® [44], C Sharp programming language, and OpenCVSharp (v. 3.4.1) [55], among others. The methodologies used for image processing and image (texture) feature extraction/mapping/selection are comprised of geometrical/statistical parameters [43,45], filtering techniques [44,53,54,58], temperature metrics [56], mathematical/perspective transforms [51,54,58], thresholding [54,56,58], masking/image binarization [54,57], augmentation [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57], synthetic/generative oversampling [46,49], among others.…”

“…The algorithms applied for classification include mathematical/statistical learning techniques (random sample consensus-RANSAC [56], Robust Principal Component Analysis-RPCA [54]), Artificial Neural Network (ANN) [43,45], convolutional neural network (CNN) [50,58], pre-trained CNN (VGG16 [44], ResNet [46,52], AlexNet [47], Effi-cientNet [48], Ensemble Learning [46], machine learning-ML (K-NN [51], SVM [48], K-fold cross-validation for fewer data [50]), CNN-ML combination (U-Net with decision tree) [57], CNN-CNN combination (UDenseNet) [49], that have relatively resulted in a perfect binary classification accuracy of 100% and a highest multiclass (four-class) diagnosis reaching 99.8% [57,58]. For georeferencing and precise localization, either aluminum identification marks on SPV panel corners are used in IRT imaging due to less emissivity and better pointing, or matching/overlapping IRT-RGB orthomosaic maps are considered [55,59].…”

Intelligent IoT Platform for Multiple PV Plant Monitoring

“…Several studies concentrated on identifying PV panel faults by measuring the I–V current [ 7 ]. As described in [ 8 , 9 ], thermal photography is also used to detect defects in PV panels. Many studies about fault diagnosis in a PV plant focused on detecting faults and anomalies that occur in the PV plant equipment [ 7 , 8 , 9 , 10 , 11 ].…”

“…As described in [8,9], thermal photography is also used to detect defects in PV panels. Many studies about fault diagnosis in a PV plant focused on detecting faults and anomalies that occur in the PV plant equipment [7][8][9][10][11].…”