Automatic defect identification of PV panels with IR images through unmanned aircraft

Tang, Cheng; Ren, Hui; Xia, Jing; Wang, Fei; Lu, Jinling

doi:10.1049/rpg2.12831

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…In [9], the authors have achieved accuracy of 77.5%. In [10], the authors claim an accuracy of 70.82%. The comparison of results is displayed in Table 2.…”

Section: Resultsmentioning

confidence: 98%

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NASNet-LSTM based Deep learning Classifier for Anomaly Detection in Solar Photovoltaic Modules

Gopalakrishnan,

Wahab,

Veerasamy

et al. 2024

J. Phys.: Conf. Ser.

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Many nations are increasing the installations of solar photovoltaic (PV) modules for clean energy production. Such PV modules are considered to be cost effective if the module’s operation lifetime is more than twenty years. In real-time, the PV modules undergo degradation due to hotspots, defects and other anomalies resulting in reduced operation lifetime. Infrared (IR) Thermography is a Non-Destructive Testing (NDT) method that can be used in identifying such anomalies present in PV modules. However, the IR thermography requires Artificial Intelligence (AI) based classification techniques to detect the anomalies. This research article proposes a deep learning classifier, based on NASNet-LSTM for the identification of electrical and non-electrical anomalies occurring in PV modules. NASNet is a convolutional neural network (CNN) based classifier when combined with Long Short-Term Memory (LSTM) Networks performs classification with an accuracy of 84.75% considering the raw dataset used in this research. The results are validated by comparing the accuracies with other models. The study concludes that NASNet-LSTM performs well in the anomaly detection of PV modules.

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“…In [9], the authors have achieved accuracy of 77.5%. In [10], the authors claim an accuracy of 70.82%. The comparison of results is displayed in Table 2.…”

Section: Resultsmentioning

confidence: 98%

“…Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) based image classification models have been used in hotspot detection of PV modules [7], [8], [9], [10]. The dataset used in this research [11] In real-time, PV systems could encounter faults due to by-pass diode failures, hotspots, cracking, etc.…”

Section: Introductionmentioning

confidence: 99%

NASNet-LSTM based Deep learning Classifier for Anomaly Detection in Solar Photovoltaic Modules

Gopalakrishnan,

Wahab,

Veerasamy

et al. 2024

J. Phys.: Conf. Ser.

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“…An accuracy of 85.35% was achieved in their studies. Tang et al [56] proposed the basic MobileNet-V3 network to realize fault classification of photovoltaic modules, Obtaining an accuracy value of the proposed method of 70.82%. Sriraman and Ramaprabha [57] used geometric transformation and generative adversarial networks image augmentation techniques for PV fault classification, Obtaining an accuracy value of 95.72% with the presented paired UdenseNet model.…”

Section: Discussionmentioning

confidence: 99%

Fault Detection in Solar Energy Systems: A Deep Learning Approach

Duranay

2023

Electronics

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While solar energy holds great significance as a clean and sustainable energy source, photovoltaic panels serve as the linchpin of this energy conversion process. However, defects in these panels can adversely impact energy production, necessitating the rapid and effective detection of such faults. This study explores the potential of using infrared solar module images for the detection of photovoltaic panel defects through deep learning, which represents a crucial step toward enhancing the efficiency and sustainability of solar energy systems. A dataset comprising 20,000 images, derived from infrared solar modules, was utilized in this study, consisting of 12 classes: cell, cell-multi, cracking, diode, diode-multi, hot spot, hot spot-multi, no-anomaly, offline-module, shadowing, soiling, and vegetation. The methodology employed the exemplar Efficientb0 model. From the exemplar model, 17,000 features were selected using the NCA feature selector. Subsequently, classification was performed using an SVM classifier. The proposed method applied to a dataset consisting of 12 classes has yielded successful results in terms of accuracy, F1-score, precision, and sensitivity metrics. These results indicate average values of 93.93% accuracy, 89.82% F1-score, 91.50% precision, and 88.28% sensitivity, respectively. The proposed method in this study accurately classifies photovoltaic panel defects based on images of infrared solar modules.

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A feature space class balancing strategy-based fault classification method in solar photovoltaic modules

Wu,

Kong,

et al. 2024

Engineering Applications of Artificial Intelligence

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Automatic defect identification of PV panels with IR images through unmanned aircraft

Cited by 5 publications

References 34 publications

NASNet-LSTM based Deep learning Classifier for Anomaly Detection in Solar Photovoltaic Modules

NASNet-LSTM based Deep learning Classifier for Anomaly Detection in Solar Photovoltaic Modules

Fault Detection in Solar Energy Systems: A Deep Learning Approach

A feature space class balancing strategy-based fault classification method in solar photovoltaic modules

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