A novel framework on intelligent detection for module defects of PV plant combining the visible and infrared images

Hong, Feng; Song, Jié; Meng, Hang; Wang, Rui; Fang, Fang; Zhang, Guangming

doi:10.1016/j.solener.2022.03.018

Cited by 43 publications

(14 citation statements)

References 17 publications

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“…Ten connectors from different strings with high temperatures of more than 50 ℃ detected through thermal imaging are taken out for measurement of contact resistance to determine the possible causes. The details are shown in Thermal imaging allows for analyzing the PV plant components without interrupting the power plant operation (Hong et al,2022;Álvarez et al,2017). Using thermal (or IR) imaging analysis, the direct approach behind fault identifications is that any error or loss in power plant components causes unusual temperature variations compared to normal operating conditions (Glavas et al, ,2017).…”

Section: Pull-out and Contact Resistance Test For Pv Plug Connectorsmentioning

confidence: 99%

RETRACTED: An Experimental Energy and Safety Analysis in a Grid-Tied Solar Photovoltaic Plant

Kapoor

Eswaramoorthy

Gopi

2023

Preprint

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The paper presents an experimental energy and safety assessments of a 2.5 MWp capacity grid-tied solar photovoltaic power plant having installed at Pune, India.. The comprehensive step-by-step assessments of visual inspection, field testing, and evaluation of reasons contributing to different energy losses, precisely the effect of soiling has been made. The field testing include in detail the testing of solar photovoltaic strings and String Combiner Box (SCB), which encompasses tests of open circuit voltage, short circuit current, earth continuity, insulation resistance, and pull-out and contact resistance test for PV plug connectors to check the efficacy of plant components and the faults in the system. The string, SCB, and inverter operational voltage, current, and power are measured and analyzed. Thermal imaging of solar photovoltaic modules and sub-components based on temperature variance gauges the faults and losses in the plant are carried out. It is found that the effect of soiling on the system's overall health and energy perspective using term Soiling Ratio approach for cleaning plants rather than a fixed cleaning cycle on power plant plant performance and efficiency. It is concluded that the research findings helpful for further research on effect of soiling ratio of existing plant.

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Section: Pull-out and Contact Resistance Test For Pv Plug Connectorsmentioning

confidence: 99%

RETRACTED: An Experimental Energy and Safety Analysis in a Grid-Tied Solar Photovoltaic Plant

Kapoor

Eswaramoorthy

Gopi

2023

Preprint

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“…The output side adopts GIOU_Loss as the loss function of the bounding box, and the weighted NMS is used to filter the bounding box to enhance the detection ability of occlusion and overlapping targets. Feng Hong et al [18] utilized the YOLOv5 + ResNet algorithm model for the detection of defects with a mAP@0.5 value of 91.7%. The detection speed reaches 36 frames per second.…”

Section: Detection Based On Yolo Series Algorithmsmentioning

confidence: 99%

Research on Surface Defect Detection Method of Photovoltaic Power Generation Panels——Comparative Analysis of Detecting Model Accuracy

Wang,

Zhang,

Zhang

et al. 2024

EAI Endorsed Trans Energy Web

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INTRODUCTION: Research on intelligent defect detection technology using machine vision was conducted to address the challenging problem of detecting and localizing PV defects in photovoltaic power generation system operation and maintenance. OBJECTIVES: The aim is to improve the accuracy of PV defect detection and enhance the operation and maintenance efficiency of PV power plants. METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection, and deep learning algorithm based detection are discussed and compared, and analyzed respectively. It is finally concluded that the deep learning based detection is more efficient in comparison. Then further analysis and simulation experiments are done through several detection algorithms based on deep learning. RESULTS: The experiment yields a high accuracy of the detection model based on the Faster-RCNN algorithm. Its mAP value reaches 92.6%. The detection model based on the YOLOv5 algorithm reaches a mAP value of 91.4%. But its speed is as much as 7 times faster than the model based on the Faster-RCNN algorithm. CONCLUSION: Comprehensive speed and accuracy index. Combining the needs of PV defect detection in the operation and maintenance of PV power generation systems with the results of simulation experiments. It is concluded that the detection model based on the YOLOv5 algorithm can provide better detection capability. Modeling with this algorithm is more suitable for PV defect detection.

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“…The core of the defect detection is the application of the YOLOv5 model. The YOLOv5 model is the state-of-the-art detection model for the real-time detection of workpiece surface, which incorporats the essence of ResNet, DenseNet, and Feature Pyramid Network (FPN) [ 27 ]. The schematic representation of the identification process is shown in Fig 4 .…”

Section: The Process Of Surface Defect Detectionmentioning

confidence: 99%

Identification and classification of surface defects for digital twin models of the workpiece

Qu,

Huang,

Zhang

et al. 2024

PLoS ONE

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Workpiece surface defect detection is an indispensable part of intelligent production. The surface information obtained by traditional 2D image detection has some limitations due to the influence of environmental light factors and part complexity. However, the digital twin model has the characteristics of high fidelity and scalability, and the digital twin surface can be obtained by a device with a scanning accuracy of 0.02mm to achieve the representation of the real surface of the workpiece. The surface defect detection system for digital twin models is proposed based on the improved YOLOv5 model in this paper. Firstly, the digital twin model of the workpiece is reconstructed by the point cloud data obtained by the scanning device, and the surface features with defects are captured. Subsequently, the training dataset is calibrated based on the defect surface, where the defect types include Inclusion, Perforation, pitting surface and Rolled-in scale. Finally, the improved YOLOv5 model with CBAM mechanism and BiFPN module was used to identify the surface defects of the digital twin model and compare it with the original YOLOv5 model and other common models. The results show that the improved YOLOv5 model can realize the identification and classification of surface defects. Compared with the original YOLOv5 model, the mAP value of the improved YOLOv5 model has increased by 0.2%, and the model has high precision. On the basis of the same data set, the improved YOLOv5 model has higher recognition accuracy than other models, improving 11.7%, 3.4%, 6.2%, 33.5%, respectively. As a result, this study provides a practical and systematic detection method for digital twin model surface during the intelligent production process, and realizes the rapid screening of the workpiece with defects.

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A novel framework on intelligent detection for module defects of PV plant combining the visible and infrared images

Cited by 43 publications

References 17 publications

RETRACTED: An Experimental Energy and Safety Analysis in a Grid-Tied Solar Photovoltaic Plant

RETRACTED: An Experimental Energy and Safety Analysis in a Grid-Tied Solar Photovoltaic Plant

Research on Surface Defect Detection Method of Photovoltaic Power Generation Panels——Comparative Analysis of Detecting Model Accuracy

Identification and classification of surface defects for digital twin models of the workpiece

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