Solar energy has received great interest in recent years, for electric power generation. ... propose a solution for PV fault detection using a deep learning method and a thermal image dataset to perform cell detection and instance ... Zou P, Li Q, Chen Y (2018) Intelligent defect detection method of photovoltaic modules based on deep learning ...
Electroluminescence (EL) imaging provides a high spatial resolution for inspecting photovoltaic (PV) cells, enabling the detection of various types of PV cell defects. …
Many methods have been proposed for detecting defects in PV cells [9], among which electroluminescence (EL) imaging is a mature non-destructive, non-contact defect detection method for PV modules, which has high resolution and has become the main method for defect detection in PV cells [10].However, manual visual assessment of EL images is time …
Therefore, this paper proposes a high-efficiency photovoltaic cell defect detection method based on improved YOLOX. First, the transfer learning strategy is adopted …
Some photovoltaic cell defects, such as cracks, have very good similarity and consistency in terms of textures, edges, and gradient variation of the gray values in the multispectral space, which are common detection targets for a multispectral map [7], [17], [18].The correlation between multispectral maps can be used to simultaneously measure multispectral …
The value of each attribute is then normalized by subtracting the mean of that ... This study has contributed to the development of an effective method for fault detection in solar energy systems, which could offer various advantages in real-world applications. ... Zhu C, Zhao X, Ahmad A (2019) CNN based automatic detection of photovoltaic cell ...
A new method for detecting PV cell cracks is proposed, which achieves higher accuracy and faster inference speed. This method enhances the YOLOv7 network to provide more effective detection in large- and small-sized PV cell cracks. Ghost module is utilized to improve the learning ability of the YOLOv7 model.
Therefore, this paper proposes a high-efficiency photovoltaic cell defect detection method based on improved YOLOX. First, the transfer learning strategy is adopted to accelerate model convergence. Secondly, to suppress the interference of complex backgrounds, the SENet attention mechanism is added to the feature extraction process.
The derived features from solar panel images provide a significant source of information for photovoltaic applications such as fault detection assessment. In this work, a method for classifying between the normal and a defective solar cell was implemented using EL imaging with selected digital image processing techniques through the Support ...
Results show that the method is able to detect faults in a PV array, and it was demonstrated experimentally for a SS-PVA. In [42] a fault detection method based on WT and ANN is developed for an ungrounded PV system. The designed method is able to detect and localise GF and LL faults in a PVA.
In this paper, we propose a deep-learning-based defect detection method for photovoltaic cells, which addresses two technical challenges: (1) to propose a method for data enhancement and category …
In view of the low efficiency and low accuracy of non-folding detection of photovoltaic power stations, Guo proposed an improved segmentation method for defective photovoltaic panels based on ...
This paper proposes an improved fusion model based on VGGNet and U-Net++, which is used for defect detection and segmentation of EL images of solar cells and shows that the defect …
Hotspot When the electrical properties of series-connected modules or cells of PV strings become mismatched, a phenomenon known as a hotspot occurs in PV cells and modules (Mellit et al. 2018 ...
With the development of machine vision, the anomaly detection technology of PV module cells is gradually transitioning to the efficient and economical automatic detection methods. Spataru et al. (2017) developed a two-dimensional matched filter to detect the location of the crack and obtained a binary location map of the micro-crack through ...
The model can better detect small target defects, meet the requirements of surface defect detection of photovoltaic cells, and proves that it has good application prospects in the field of ...
The experimental data were obtained from the dataset published by Buerhop et al. [34] at the 35th European Photovoltaic Solar Energy Conference and Exhibition, which provided solar cell images extracted from high-resolution electroluminescence images of photovoltaic modules. The datasets were obtained from 44 different PV modules, of which 18 ...
In recent years, with the rapid development of artificial intelligence, deep learning-based EL imaging techniques for PV cell defect detection have emerged as effective, accurate, and fast …
In order to detect PV cell defects faster and better, a technology called the PV cell Defects DEtection Transformer (PD-DETR) is proposed. To address the issue of slow …
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.
PDF | On Feb 1, 2020, Ronnie O. Serfa Juan and others published Photovoltaic Cell Defect Detection Model based-on Extracted Electroluminescence Images using SVM Classifier | Find, read and cite ...
To address the challenging issue of detecting surface imperfections in photovoltaic cells, several methods based on artificial intelligence have ... M.W., et al.: CNN based automatic detection of photovoltaic cell defects in electroluminescence images. Energy 189(C), 116319 (2019 ... Discover content. Journals A-Z; Books A-Z; Publish with us.
Fast fault detection method for photovoltaic arrays with adaptive deep multiscale feature enhancement. ... PV cell degradation [12] ... The second type of fault diagnosis method is to use the comparison value between the theoretical model and the actual value to directly obtain the operating variable threshold based on the expert knowledge base ...
Compared to other methods, the proposed VarifocalNet has the highest detection accuracy and has a faster detection speed than other methods except for the DDH-YOLOv5 method and the improved YOLOv7 method. Detecting and replacing defective photovoltaic modules is essential as they directly impact power generation efficiency. Many current deep …
Only Look Once version 7 (YOLOv7) model is developed for the detection of cell cracks in PV modules. Detecting small cracks in PV modules is a challenging task. These cracks can occur during production, installation and operation stages. ... Fig.1 The flowchart of the PV crack detection system • A new method for detecting PV cell cracks is ...
In this study, we propose a weakly supervised learning method to build a CNN for cell-level defect detection in a cost-efficient manner. Our method uses a training dataset solely with module-level annotations indicating whether each PV module contains defective cells, thereby substantially reducing the required annotation costs.
A hot spot detection method for PV modules based on image semantic segmentation is proposed, which introduces migration network in the image processing network to improve the detection accuracy of the model. Hot spots of solar cells are one of the most probable failures during the operation of PV(Photovoltaic) systems. Hot spot can lead to loss of power …
The ablation study demonstrates that our CCT and PSA modules enhance the detection accuracy of YOLOv8 in photovoltaic cell anomaly detection tasks. Table 2 Ablation study. Full size table
The systems contain a PV cell array, inverter, coupling transformers, RLC load and grid-connected through the utility circuit breaker. The generated power from the PV array is 100 kW at 1000w/m 2 irradiance and 25 °C temperature. A boost converter with switching frequency of 5 kHz is used to increase the voltage of PV from 272.4 to 500 V.
The detection methods of PV cells defects usually include electrical current-voltage (I–V) curves [7], electroluminescence (EL) [8], visual inspection [9] and so on. Infrared thermography (IRT) method is widely used because of its advantages of non-contact, large measurement area and good measurement effect [10].
In view of the reduced power generation efficiency caused by ash or dirt on the surface of photovoltaic panels, and the problems of heavy workload and low efficiency faced by manual detection, this study proposes a method to detect dust or dust on the surface of photovoltaic cells with the help of image processing technology to timely eliminate hidden …
Recently, convolutional neural networks (CNNs) have proven successful in automating the detection of defective photovoltaic (PV) cells within PV modules.
In the process of the decarbonization of energy production, the use of photovoltaic systems (PVS) is an increasing trend. In order to optimize the power generation, the fault detection and identification in PVS is significant. The purpose of this work is the study and implementation of such an algorithm, for the detection as many as faults arising on the DC …
Abstract: The multiscale defect detection for photovoltaic (PV) cell electroluminescence (EL) images is a challenging task, due to the feature vanishing as network deepens. To address …
Zhang et al. 8 introduced a photovoltaic cell defect detection method leveraging the YOLOV7 model, which is designed for rapid detection. They enhanced the model''s feature extraction ...
DOI: 10.1016/j.jobe.2023.106375 Corpus ID: 257948055; A photovoltaic surface defect detection method for building based on deep learning @article{Cao2023APS, title={A photovoltaic surface defect detection method for building based on deep learning}, author={Yukang Cao and Dandan Pang and Yi Yan and Yongqing Jiang and Chongyi Tian}, journal={Journal of Building …
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.
The model can better detect small target defects, meet the requirements of surface defect detection of photovoltaic cells, and proves that it has good application prospects in the field of ...
A photovoltaic cell defect polarization imaging small target detection method based on improved YOLOv7 is proposed to address the problem of low detection accuracy caused by insufficient feature extraction ability in the process of small target defect detection. Firstly, polarization imaging technology is introduced, using polarization degree images as …
This paper proposes a novel PV defect detection method using attention mechanisms and transformers within the YOLOv8 object detection framework.
PDF | On Jan 1, 2018, Binbin Ni and others published Intelligent Defect Detection Method of Photovoltaic Modules Based on Deep Learning | Find, read and cite all the research you need on ResearchGate
The research was presented in "Research on detection method of photovoltaic cell surface dirt based on image processing technology," published in Scientific Reports. The group was formed by ...
Different statistical outcomes have affirmed the significance of Photovoltaic (PV) systems and grid-connected PV plants worldwide. Surprisingly, the global cumulative installed capacity of solar PV systems has massively increased since 2000 to 1,177 GW by the end of 2022 [1].Moreover, installing PV plants has led to the exponential growth of solar cell …
Common detection methods for surface fouling of photovoltaic panels include current–voltage curve analysis 2, reection spectrum analysis 3, electrochemical impedance spectrum analysis 4 ...
Therefore, this paper proposes a high-efficiency photovoltaic cell defect detection method based on improved YOLOX. First, the transfer learning training strategy is …
