IR Thermal Image Analysis: An Efficient Algorithm for Accurate Hot-Spot Fault Detection and Localization in Solar Photovoltaic Systems

Alajmi, Masoud; Awedat, Khalfalla; Aldeen, M.; Alwagdani, Salman

doi:10.1109/eit.2019.8833855

Cited by 12 publications

(15 citation statements)

References 10 publications

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“…Zhang et al [15] used a high-resolution infrared thermal imager to detect outdoor PV array faults and demonstrated that infrared images can clearly show defective solar cells or PV arrays. Masoud et al [16] developed an efficient technique using infrared thermal energy analysis to detect and locate hotspot faults using MATLAB, which resulted in fast and accurate error detection. Starting from the K-mean clustering method, Ngo et al [17] proposed a method for contour extraction and hotspot detection of infrared PV images.…”

Section: Introductionmentioning

confidence: 99%

Real-time Fault Diagnosis of Photovoltaic Modules for Integrated Energy Systems Based on YOLOv7

Jin¹,

Shi

Li³

et al. 2023

Preprint

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The proposal of the double carbon goal in China has developed its renewable energy on a large scale and rapidly increased the installed capacity of its photovoltaic (PV) power stations. The reliability of PV modules, as the core of PV power generation, affects the safety and stability of the whole system. At present, the fault diagnosis and location technology of PV modules has high cost, low efficiency, and low accuracy. This study proposed a real-time fault diagnosis of PV modules for integrated energy systems based on YOLOv7. The infrared images of PV modules in distributed PV power stations were obtained using drone cruise photography, and the fault points in the infrared images were identified and marked by training the YOLOv7 network. Compared with the traditional SSD and Faster-RCNN models, YOLOv7 not only ensures the accuracy of fault diagnosis but also greatly improves the detection speed of the model as demonstrated through actual data verification. The average accuracy reaches 95.4%, and the recall rate is 97.8%. The infrared image processing speeds are increased by 30.8% and 42.8%. Compared with traditional detection methods, the proposed method can achieve real-time fault diagnosis and play a greater role in the practical application of large-scale PV power stations.

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Section: Introductionmentioning

confidence: 99%

Real-time Fault Diagnosis of Photovoltaic Modules for Integrated Energy Systems Based on YOLOv7

Jin¹,

Shi

Li³

et al. 2023

Preprint

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show abstract

“…shows the different input images on which the comparisons were conducted. The comparison has been conducted between the results of (b) K-Means segmentation [45], (c) K-Means segmentation for photovoltaic hotspot detection [46], (d) HSV thresholding [50], (e) Multilevel Otsu Segmentation [47], and (f) our method for hotspot identification. Each column shows the results of the aforementioned techniques on a particular input image.…”

Section: List Of Figuresmentioning

confidence: 99%

“…. [45], (c) K-Means segmentation for photovoltaic hotspot detection [46], (d) HSV thresholding [50], (e) Multi-level Otsu segmentation [47], (f) Self-supervised hotspot identification [72] learning models and neural networks on the TIHD dataset . .…”

Section: List Of Figuresmentioning

confidence: 99%

“…. 2.5 Dice coefficient averages of the results outputted by the existing hotspot identification techniques -K-Means [45], K-Means for photovoltaic panels [46], HSV Thresholding [50] and Multilevel Otsu [47]. .…”

Section: List Of Figuresmentioning

confidence: 99%

“…. K-Means [45], K-Means for photovoltaic panels [46], HSV thresholding [50], Multilevel Otsu [47], Self-supervised learning [72], and ours. .…”

Section: List Of Figuresmentioning

confidence: 99%

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Thermal image analytics for industrial anomaly detection

Goyal

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I extend my deepest appreciation to my mother and brother, whose unwavering support, motivation, and presence have been a constant source of strength throughout this academic endeavour. Their encouragement and belief in my capabilities have played a pivotal role in overcoming challenges and reaching milestones. I am also grateful for the enduring inspiration provided by my father, whose legacy continues to guide me in all aspects of life. His influence has been a beacon of inspiration, and I dedicate this achievement to his memory.

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Exploring the Evolution of Fault Detection Techniques in Renewable Energy Systems: An Analysis of Traditional Methods and Deep Learning Approaches

Siavash-Abkenari,

Azad,

Nazari-Heris

2024

Power Systems

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IR Thermal Image Analysis: An Efficient Algorithm for Accurate Hot-Spot Fault Detection and Localization in Solar Photovoltaic Systems

Cited by 12 publications

References 10 publications

Real-time Fault Diagnosis of Photovoltaic Modules for Integrated Energy Systems Based on YOLOv7

Real-time Fault Diagnosis of Photovoltaic Modules for Integrated Energy Systems Based on YOLOv7

Thermal image analytics for industrial anomaly detection

Exploring the Evolution of Fault Detection Techniques in Renewable Energy Systems: An Analysis of Traditional Methods and Deep Learning Approaches

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