Hot Spot Detection of Photovoltaic Module Infrared Near-field Image based on Convolutional Neural Network

Fan, Fu; Na, ZhiXiong; Zhang, Changzhi; Li, HaoRan; Tong, ChaoLiu

doi:10.1088/1742-6596/2310/1/012076

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…By associating temperatures with each pixel, a quantitative assessment of the module’s temperature distribution can be obtained [ 35 ]. Furthermore, the classification of the overall state of the PV module can be performed, based on the temperatures detected.…”

Section: Methodsmentioning

confidence: 99%

IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances

Cardinale-Villalobos,

Jimenez-Delgado,

García-Ramírez

et al. 2023

Sensors

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Infrared thermography (IRT) is a technique used to diagnose Photovoltaic (PV) installations to detect sub-optimal conditions. The increase of PV installations in smart cities has generated the search for technology that improves the use of IRT, which requires irradiance conditions to be greater than 700 W/m2, making it impossible to use at times when irradiance goes under that value. This project presents an IoT platform working on artificial intelligence (AI) which automatically detects hot spots in PV modules by analyzing the temperature differentials between modules exposed to irradiances greater than 300 W/m2. For this purpose, two AI (Deep learning and machine learning) were trained and tested in a real PV installation where hot spots were induced. The system was able to detect hot spots with a sensitivity of 0.995 and an accuracy of 0.923 under dirty, short-circuited, and partially shaded conditions. This project differs from others because it proposes an alternative to facilitate the implementation of diagnostics with IRT and evaluates the real temperatures of PV modules, which represents a potential economic saving for PV installation managers and inspectors.

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Section: Methodsmentioning

confidence: 99%

IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances

Cardinale-Villalobos,

Jimenez-Delgado,

García-Ramírez

et al. 2023

Sensors

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“…The model improves the Faster-RCNN's poor detection of small targets and achieves an average detection accuracy of 97% on the test set. Literature [13] combines Faster-RCNN with Spot FPN multi-scale feature learning module. The detection of small targets in the model can be improved by adding the Spot FPN structure, and the accuracy of the model can be improved by using the auxiliary loss function and the primary loss function to predict together, and its average accuracy is improved by about 3% compared to the preimprovement period.…”

Section: Faster-rcnn Based Detectionmentioning

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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“…based on the application and the industry. For instance, work has been done on detection of hotspots in solar panels [19,20,21], detection of cracks in roads, metals and rooftops [22,23,24,25], detection of hotspots in electric equipment or machinery, etc. The motivation for this being the automatic early identification of an oncoming failure, for prompting timely repair, thereby preventing financial, human and resource losses.…”

Section: Deep Learning and Thermal Imaging Analyticsmentioning

confidence: 99%

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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Hot Spot Detection of Photovoltaic Module Infrared Near-field Image based on Convolutional Neural Network

Cited by 6 publications

References 16 publications

IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances

IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances

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

Thermal image analytics for industrial anomaly detection

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