A review of automated solar photovoltaic defect detection
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
Improved Solar Photovoltaic Panel Defect Detection
Solar photovoltaic panel defect detection is an important part of solar photovoltaic panel quality inspection. In view of the characteristics of irregular feature size of photovoltaic panels and dense distribution of small targets, Ghostconv is used instead of traditional Conv in the Backbone backbone model of the model, and the C2f module
Photovoltaics Cell Anomaly Detection Using Deep Learning
A significant challenge in energy system cyber security is the current inability to detect cyber-physical attacks targeting and originating from distributed grid-edge devices such as photovoltaics
Fast object detection of anomaly photovoltaic (PV) cells using
Anomaly detection in photovoltaic (PV) cells is crucial for ensuring the efficient operation of solar power systems and preventing potential energy losses. In this paper, we
Distributed solar photovoltaic array location and extent dataset
Design Type(s) data integration objective • observation design Measurement Type(s) solar photovoltaic array location Technology Type(s) digital curation Factor Type(s) Sample Characteristic(s
A novel method for fault diagnosis in photovoltaic arrays used in
This study addresses the critical issue of fault diagnosis in photovoltaic (PV) arrays, considering the increasing integration of distributed PV systems into power grids. The research employs a novel approach that combines artificial neural networks, specifically radial basis functions (RBFs), with machine learning techniques. The methodology involves training
Hot Spot Detection of Photovoltaic Module Based on
To solve the problems of low detection efficiency, low accuracy, and difficulty of distributed hot spot detection, a hot spot detection method using a photovoltaic module based on the distributed
Hot Spot Detection of Photovoltaic Module Based on Distributed
Photovoltaic cell single-diode model: ( a ) normal photovoltaic cell; ( b ) hot spot cell. The I-V curvilinear equation is obtained on the basis of the equivalent circuit model of the photovoltaic
PV Identifier: Extraction of small-scale distributed photovoltaics in
In this study, we propose an advanced deep learning model, called PV Identifier, to enhance the identification accuracy of small-scale PV systems from HSRRS images. PV
Photovoltaic Cell Anomaly Detection Enabled by Scale Distribution
Request PDF | Photovoltaic Cell Anomaly Detection Enabled by Scale Distribution Alignment Learning and Multi-Scale Linear Attention Framework | The growing prevalence of photovoltaic (PV) systems
Novel Photovoltaic Hot-spotting Fault Detection Algorithm
1 hot-spotted solar cell in a PV module: 1058 2 hot-spotted solar cells in a PV module: 491 3 hot-spotted solar cells in a PV module: 542 4 hot-spotted solar cell in a PV module: 283 ≥5 hot-spotted solar cell in a PV module: 155 Fig. 1. Geographical map for the PV sites locations used in the analysis Fig. 2.
A PV cell defect detector combined with transformer and attention
Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor
Analyzing Potential Induced Degradation
This process essentially "pollutes" the cell, diminishing its photovoltaic effect and resulting in power losses. PID effects can lead to significant power losses, potentially reaching up to
Distributed solar photovoltaic power prediction algorithm based
The photovoltaic power generation system is constructed based on the working principal diagram of the solar cell, as shown in Fig. 2 nversely, in conditions of insufficient sunshine, particularly at night, if the electric energy required by the local load exceeds the AC electric energy generated by the photovoltaic system, the grid will automatically provide
Enhanced photovoltaic panel defect detection via
Detecting defects on photovoltaic panels using electroluminescence images can significantly enhance the production quality of these panels. Nonetheless, in the process of defect detection, there
Biomimetic model of photovoltaic cell defect detection based on
Photovoltaic (PV) cells are an important device for converting solar energy into electrical energy and are therefore widely used in the field of renewable energy [1].However, PV cells are prone to a variety of potential defect problems, and the main reason for these defects is that PV cells undergo mechanical stresses during the production and subsequent transport
A Review on Defect Detection of
The past two decades have seen an increase in the deployment of photovoltaic installations as nations around the world try to play their part in dampening the impacts of global
Polycrystalline silicon photovoltaic cell defects detection based on
To address these challenges, we propose a novel deep convolutional neural network (CNN) model for effectively identifying small target defects in polycrystalline PV cells.
PV-YOLO: Lightweight YOLO for Photovoltaic Panel Fault Detection
The rapid development of the photovoltaic industry in recent years has made the efficient and accurate completion of photovoltaic operation and maintenance a major focus in recent studies. The key to photovoltaic operation and maintenance is the accurate multifault identification of photovoltaic panel images collected using drones. In this paper, PV-YOLO is proposed to
Hot Spot Detection of Photovoltaic Module Based on Distributed
The detection system can obtain the surface temperature distribution of the photovoltaic panel in real time and can effectively identify and locate the hot spot effect of photovoltaic cells. (2) The temperature of photovoltaic cells with a hot spot effect is
A Multi-scale neighbourhood feature interaction network for
6 天之前· Thanks to the powerful representation capacities of deep CNNs [33], and the industrial demand for efficient defect detection, YOLO-based approaches have demonstrated outstanding performance in PV cell detection [34], [35]. Recently, a Yolov5-based network was developed to accurately detect defects in PV cells [36], [37], [38]. These deep
Photovoltaic Cell Anomaly Detection Enabled by Scale Distribution
The growing prevalence of the photovoltaic (PV) systems has intensified the focus on fault prediction and health management within both the academic and industrial realms. Electroluminescence (EL) imaging technology, recognized as an advanced detection method, has substantiated its efficiency and practicality in identifying diverse defects. In this study, we
Distributed Photovoltaic Communication Anomaly Detection
As distributed photovoltaic (PV) technology rapidly develops and is widely applied, the methods of cyberattacks are continuously evolving, posing increasingly severe threats to the communication networks of distributed PV systems. Recent studies have shown that the Transformer model, which effectively integrates global information and handles long
Hot spot detection and prevention using a
After detection of hot spotting, a remedial active strategy is required to prevent permanent damage of PV panel cells. In, an interesting and simple technique has been
Effective transfer learning of defect detection for photovoltaic
In Xie et al. (2023) the issue of solar cell defect detection is discussed, which is challenging due to variations in production schemes and impurities on the surface of polycrystalline cells. To address this problem, the authors proposed a transfer learning approach with an adversarial domain discriminator and attention-based transfer learning.
Photovoltaics Cell Anomaly Detection Using Deep Learning
This paper introduces a state-of-the-art defect detection model based on the Yolo v.7 architecture designed explicitly for photovoltaic cell electroluminescence images.
Improved YOLOv8-GD deep learning model for defect detection
Photovoltaic defect detection is an essential aspect of research on building-distributed photovoltaic systems. Existing photovoltaic defect detection models based on deep learning, such as YOLOv5 and YOLOv8, have significantly improved the accuracy of photovoltaic defect detection.
Deep-Learning-Based Automatic Detection of Photovoltaic Cell
To detect defects on the surface of PV cells, researchers have proposed methods such as electrical characterization, electroluminescence imaging [7,8,9], infrared (IR) imaging, etc. EL imaging is frequently utilized in solar cell surface detection studies because it is rapid, non-destructive, simpler and more practical to integrate into actual manufacturing
A photovoltaic cell defect detection model capable of
We propose a photovoltaic cell defect detection model capable of extracting topological knowledge, aggregating local multi-order dynamic contexts, and effectively...
Enhanced Fault Detection in Photovoltaic
Solar photovoltaic systems have increasingly become essential for harvesting renewable energy. However, as these systems grow in prevalence, the issue of the end of life
Deep Learning-Based Defect Detection for Photovoltaic Cells
This paper delves into the application of deep learning techniques for the classification of PV cell defects using electroluminescence images. Deep learning, a subset of
Adaptive automatic solar cell defect detection and classification
Adaptive solar cell defect detection: Since the solar cell has the same area in the series of EL images and the position of defects is unchanged, Solar cell modeling: The 2-D distributed circuit network model of the solar cell is established using the simulation program with integrated circuit emphasis (SPICE) software. It consists of a
Photovoltaic Cell Defect Detection Based on Personalized Update
In the distributed photovoltaic cell defect detection scenario, we often encounter the problem of data privacy and non-independent and identically distributed d
Leveraging dynamic power benchmarks and CUSUM charts
Faults in photovoltaic (PV) systems are common during their operational lifetime. Existing PV fault detection methods, which are primarily designed for large-scale PV fields, struggle with distributed systems due to their reliance on on-site weather sensors and inability to handle complex local shading effects on PV performance in the built environment.
An improved feature aggregation network for photovoltaic cell
2 天之前· Detecting defects in photovoltaic cells is essential for maintaining the reliability and efficiency of solar power systems. Existing methods face challenges such as (1) the interaction
6 FAQs about [Distributed photovoltaic cell detection]
Can a photovoltaic cell defect detection model extract topological knowledge?
We propose a photovoltaic cell defect detection model capable of extracting topological knowledge, aggregating local multi-order dynamic contexts, and effectively capturing diverse defect features, particularly for small flaws.
Can convolutional neural networks detect photovoltaic cell defects?
As shown in Fig. 20, detecting small-scale defects poses a significant challenge in photovoltaic cell defect detection. Due to the low contrast in electroluminescence images, conventional convolutional neural networks tend to miss these features, resulting in missed or false detections.
Can a defect detection model handle photovoltaic cell electroluminescence images?
However, traditional object detection models prove inadequate for handling photovoltaic cell electroluminescence (EL) images, which are characterized by high levels of noise. To address this challenge, we developed an advanced defect detection model specifically designed for photovoltaic cells, which integrates topological knowledge extraction.
What are the limitations of photovoltaic cell defect detection?
This limitation is particularly critical in the context of photovoltaic (PV) cell defect detection, where accurate detection requires resolving small-scale target information loss and suppressing noise interference.
Does graph inference work in photovoltaic cell defect detection?
Graph inference techniques have demonstrated remarkable performance in photovoltaic (PV) cell defect detection tasks. Liu et al. 38 introduced a convolutional neural network (CNN)-based model that incorporates a novel channel attention mechanism implemented via graph convolution.
Can automated defect detection improve photovoltaic production capacity?
Scientific Reports 14, Article number: 20671 (2024) Cite this article Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor-intensive and costly manual inspections and enhancing production capacity.