Hence, depending upon the designing approaches, solar cell technology may be classified into two categories, i.e., crystalline Si-based technology and thin film-based technology, which are as...
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Classification of photovoltaic cell based on PV material [21]. This review paper presents the study of photovoltaic cells for solar-powered aircraft...
Solar cell defects are divided into seven classes such as one non-defective and six defective classes. Feature extraction algorithms such as histograms of oriented gradients (HOG), KAZE, Scale-Invariant Feature
Therefore, this paper aims to develop a deep learning (DL) system that can accurately classify and detect defects in Electrouminescent (EL) images of PV cells, more specifically through implementing Convolutional Neural Networks CNN. Surprisingly, the suggested PV fault detection model outperformed commercially available PV fault detection
Quick fault diagnoses in photovoltaic powerhouses are significant to continue working with high efficiency and without risk of intense damage. Using a simple deep learning
Hence, depending upon the designing approaches, solar cell technology may be classified into two categories, i.e., crystalline Si-based technology and thin film-based technology, which are
Solar PV cell technologies are generally classified as thin-film solar PV cell technology, Wafer-based crystalline solar PV cell technology and other recently emerging technologies.
Download scientific diagram | Classification of photovoltaic system from publication: Performance of grid-connected solar photovoltaic power plants in the Middle East and North Africa | A
This study focuses on improving the classification performance and reducing the complexity of CNN models for classifying faults in infrared images of PV modules. A novel TLDR-CNN approach is developed to achieve these objectives. In addition, the effectiveness of the proposed approach is verified using Grad-CAM technology, which can
The anomaly detection in photovoltaic (PV) cell electroluminescence (EL) image is of great significance for the vision-based fault diagnosis. Many researchers are committed to solving this problem
In this study, a deep convolutional neural network (CNN) model using residual connections and spatial pyramid pooling (SPP) is proposed for the efficient classification of PV cell defects. The proposed CNN model is built on the Inception-v3 network.
Solar PV cell technologies are generally classified as thin-film solar PV cell technology, Wafer-based crystalline solar PV cell technology and other recently emerging technologies.
This work builds a PV EL Anomaly Detection dataset for polycrystalline solar cell, which contains 36 543 near-infrared images with various internal defects and heterogeneous background and carries out a comprehensive evaluation of the state-of-the-art object detection methods based on deep learning. The anomaly detection in photovoltaic (PV) cell
Photovoltaic (PV) cells are a major part of solar power stations, and the inevitable faults of a cell affect its work efficiency and the safety of the power station. During manufacturing and service, it is necessary to carry out fault detection and classification. A convolutional-neural-network (CNN)-architecture-based PV cell fault
Solar cell defects are divided into seven classes such as one non-defective and six defective classes. Feature extraction algorithms such as histograms of oriented gradients (HOG), KAZE, Scale-Invariant Feature Transform (SIFT) and speeded-up-robust features (SURF) are used to train the SVM classifier. Finally, the performance results are
This study focuses on improving the classification performance and reducing the complexity of CNN models for classifying faults in infrared images of PV modules. A novel
This paper presents a data-driven anomaly detection and classification solution, which can accurately detect and classify diverse photovoltaic system anomalies and has been deployed in two large-scale solar farms. Operation anomalies are common phenomena in large-scale solar farms. Effective anomaly detection and classification is essential for improving
Photovoltaic (PV) cells are made of at least two layers of semiconducting material, usually silicon, doped with special additives. Electroluminescence (EL) imaging is a useful modality for the
Therefore, this paper aims to develop a deep learning (DL) system that can accurately classify and detect defects in Electrouminescent (EL) images of PV cells, more
Photovoltaic (PV) cells are made of at least two layers of semiconducting material, usually silicon, doped with special additives. Electroluminescence (EL) imaging is a useful modality for the inspection of photovoltaic (PV) modules.
Quick fault diagnoses in photovoltaic powerhouses are significant to continue working with high efficiency and without risk of intense damage. Using a simple deep learning-based method for fault diagnosis in this study revealed the high efficiency and ability of DNNs on photovoltaic thermal image classification. For this purpose
This convolutional-neural-network (CNN)-architecture-based PV cell fault classification method is proposed and trained on an infrared image data set and has high application potential in automatic fault identification and classification. Photovoltaic (PV) cells are a major part of solar power stations, and the inevitable faults of a cell affect its work efficiency
FAULT DIAGNOSIS AND CLASSIFICATION OF LARGE-SCALE PHOTOVOLTAIC PLANTS THROUGH AERIAL ORTHOPHOTO THERMAL MAPPING John A. Tsanakas*, Godefroy Vannier, Alexandre Plissonnier, Duy Long Ha, Franck Barruel
To explore the evolution and classification of photovoltaic (PV) cell technology and examine three distinct generations to understand their emergence and development processes. • To explore the operating mechanisms and device architectures of OPV cells. Compare their structures and evaluate their advantages and disadvantages. • To review the electrical properties,
Automatic defect classification in photovoltaic (PV) modules is gaining significant attention due to the limited application of manual/visual inspection. However, the automatic classification of defects in crystalline silicon solar cells is a challenging task due to the inhomogeneous intensity of cell cracks and complex background. The present
Explainable Photovoltaic Cell Defect Classification from Electroluminescence Images using Modern Deep Learning Technique grading of the photovoltaic cells is imperative for accurate quantification and prediction of the energy in large scale solar plants. Moreover, the quality of the photovoltaic materials and the presence of faults considerably influence the electricity
Photovoltaic (PV) cells are a major part of solar power stations, and the inevitable faults of a cell affect its work efficiency and the safety of the power station. During
Classification of photovoltaic cell based on PV material [21]. This review paper presents the study of photovoltaic cells for solar-powered aircraft...
Quick fault diagnoses in photovoltaic powerhouses are significant to continue working with high efficiency and without risk of intense damage. Using a simple deep learning-based method for fault diagnosis in this study revealed the high efficiency and ability of DNNs on photovoltaic thermal image classification.
A hybrid deep CNN architecture is proposed to achieve high classification performance in PV solar cell defects. The proposed method is based on the integration of residual connections into the inception network. Therefore, the advantages of both structures are combined and multi-scale and distinctive features can be extracted in the training.
To classify the seven types of defects in a polycrystalline silicon PV cell, the proposed machine learning approaches are applied to the public dataset of solar cell EL images. The successful classification of these defects is a challenging task due to the background texture of the cells.
In the classification of PV cell defect problems, it is a challenging topic to obtain and analyze a general dataset containing multi-class defects. For this purpose, a comprehensive and large-scale EL image dataset is used to evaluate the proposed method.
In this paper, residual-connection-based Inception-v3 with SPP structure (Res-Inc-v3-SPP) is proposed to classify faults in the PV module cells based on EL imaging. The proposed method is improved the classification performance and stability by integrating the residual connection and SPP into the inception network.
The defect classification accuracy results for PV cell EL images are obtained using feature extraction techniques such as HOG, KAZE, SIFT, and SURF. SVM models are trained for each technique to obtain the best accuracy results. The input data for these models are EL images with a resolution of pixels.
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