Detecting Snow Layer on Solar Panels using Deep Learning

Ozturk, Oktay; Hangun, Batuhan; Eyecioğlu, Önder

doi:10.1109/icrera52334.2021.9598700

Cited by 8 publications

(1 citation statement)

References 15 publications

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“…According to the maps generated on temperature, the presence of defects has been detected. A CNN-based deep learning method YOLO [24,25] uses drones to detect the snowy layers in solar panels. This method segments the panel images and evaluates the PV images with the use of the existing network to detect the defects.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Model for Defect Detection and Localization on Photovoltaic Panels

Prabhakaran¹,

Uthra²,

Preetharoselyn³

2023

Computer Systems Science and Engineering

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The Problem of Photovoltaic (PV) defects detection and classification has been well studied. Several techniques exist in identifying the defects and localizing them in PV panels that use various features, but suffer to achieve higher performance. An efficient Real-Time Multi Variant Deep learning Model (RMVDM) is presented in this article to handle this issue. The method considers different defects like a spotlight, crack, dust, and micro-cracks to detect the defects as well as localizes the defects. The image data set given has been preprocessed by applying the Region-Based Histogram Approximation (RHA) algorithm. The preprocessed images are applied with Gray Scale Quantization Algorithm (GSQA) to extract the features. Extracted features are trained with a Multi Variant Deep learning model where the model trained with a number of layers belongs to different classes of neurons. Each class neuron has been designed to measure Defect Class Support (DCS). At the test phase, the input image has been applied with different operations, and the features extracted passed through the model trained. The output layer returns a number of DCS values using which the method identifies the class of defect and localizes the defect in the image. Further, the method uses the Higher-Order Texture Localization (HOTL) technique in localizing the defect. The proposed model produces efficient results with around 97% in defect detection and localization with higher accuracy and less time complexity.

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Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Model for Defect Detection and Localization on Photovoltaic Panels

Prabhakaran¹,

Uthra²,

Preetharoselyn³

2023

Computer Systems Science and Engineering

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Transfer Learning Based Flat Tire Detection by Using RGB Images

Ozturk

HANGÜN>²

2023

Engineering Cyber-Physical Systems and Critical Infrastructures

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An interpretable Bayesian deep learning-based approach for sustainable clean energy

Ezzat,

Ahmed,

Soliman

et al. 2024

Neural Comput & Applic

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Sustainable Development Goal 7 is dedicated to ensuring access to clean and affordable energy that can be utilized in various applications. Solar panels (SP) are utilized to convert sunlight into electricity, acting as a renewable energy source. It is important to keep SP clean to obtain the required performance, as the accumulation of snow and dust on SP greatly affects the amount of electricity generated. On the other hand, excessive cleaning has some detrimental effects on the SP, therefore cleaning should only be done when necessary and not on a regular basis. Consequently, it is critical to determine whether the cleaning procedure is necessary by automatically detecting the presence of dust or snow on the panels while avoiding inaccurate predictions. Research efforts have been made to detect the presence of dust and snow on SP, but most of the proposed methods do not guarantee accurate detection results. This paper proposes an accurate, reliable, and interpretable approach called Solar-OBNet. The proposed Solar-OBNet can detect dusty SP and snow-covered SP very efficiently and be used in conjunction with the methods used to clean SP. The proposed Solar-OBNet is based on a Bayesian convolutional neural network, which enables it to express the amount of confidence in its predictions. Two measurements are used to estimate the uncertainty in the outcomes of the proposed Solar-OBNet, namely predictive entropy and standard deviation. The proposed Solar-OBNet can express confidence in the correct predictions by showing low values for predictive entropy and standard deviation. The proposed Solar-OBNet can also give an uncertainty warning in the case of erroneous predictions by showing high values of predictive entropy and standard deviation. The proposed Solar-OBNet’s efficacy was verified by interpreting its results using a method called Weighted Gradient-Directed Class Activation Mapping (Grad-CAM). The proposed Solar-OBNet has achieved a balanced accuracy of 94.07% and an average specificity 95.83%, outperforming other comparable methods.

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Detecting Snow Layer on Solar Panels using Deep Learning

Cited by 8 publications

References 15 publications

Deep Learning-Based Model for Defect Detection and Localization on Photovoltaic Panels

Deep Learning-Based Model for Defect Detection and Localization on Photovoltaic Panels

Transfer Learning Based Flat Tire Detection by Using RGB Images

An interpretable Bayesian deep learning-based approach for sustainable clean energy

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