SolNet: A Convolutional Neural Network for Detecting Dust on Solar Panels

Onim, Md. Saif Hassan; Sakif, Zubayar Mahatab Md; Ahnaf, Adil; Kabir, Ahsan; Azad, A.K.; Oo, Amanullah Maung Than; Afreen, Rafina; Hridy, Sumaita Tanjim; Hossain, Mahtab; Jabid, Taskeed; Ali, Md Sawkat

doi:10.3390/en16010155

Cited by 28 publications

(10 citation statements)

References 50 publications

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“…The model used in the approach in [79] has a lower computational complexity and trainable parameters while achieving an accuracy of 98.2% on the test set, surpassing the current state-of-the-art models. The categories within the dataset could be expanded in the future to include different degrees and types of dust accumulation and to collect more diverse and larger-scale image data under different environmental conditions.…”

Section: Overlay Detection Technology Based On Deep Learningmentioning

confidence: 99%

“…M. S. H. Onim et al [79] introduce a novel dataset of images of solar panels in clean and dusty conditions and use it to benchmark several state-of-the-art (SOTA) discriminant analysis algorithms. The authors also propose a new CNN architecture, SOLNET, which outperforms the existing SOTA algorithms in terms of accuracy and efficiency.…”

Section: Overlay Detection Technology Based On Deep Learningmentioning

confidence: 99%

“…Ref. [79] require a lot of computing resources and time to train and test the model, and cannot distinguish between different degrees or types of dust. Refs.…”

Section: Overlay Detection Technology Based On Deep Learningmentioning

confidence: 99%

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A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

Yang,

Sun,

Zou

et al. 2024

Energies

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Photovoltaic (PV) panels are prone to experiencing various overlays and faults that can affect their performance and efficiency. The detection of photovoltaic panel overlays and faults is crucial for enhancing the performance and durability of photovoltaic power generation systems. It can minimize energy losses, increase system reliability and lifetime, and lower maintenance costs. Furthermore, it can contribute to the sustainable development of photovoltaic power generation systems, which can reduce our reliance on conventional energy sources and mitigate environmental pollution and greenhouse gas emissions in line with the goals of sustainable energy and environmental protection. In this paper, we provide a comprehensive survey of the existing detection techniques for PV panel overlays and faults from two main aspects. The first aspect is the detection of PV panel overlays, which are mainly caused by dust, snow, or shading. We classify the existing PV panel overlay detection methods into two categories, including image processing and deep learning methods, and analyze their advantages, disadvantages, and influencing factors. We also discuss some other methods for overlay detection that do not process images to detect PV panel overlays. The second aspect is the detection of PV panel faults, which are mainly caused by cracks, hot spots, or partial shading. We categorize existing PV panel fault detection methods into three categories, including electrical parameter detection methods, detection methods based on image processing, and detection methods based on data mining and artificial intelligence, and discusses their advantages and disadvantages.

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Section: Overlay Detection Technology Based On Deep Learningmentioning

confidence: 99%

Section: Overlay Detection Technology Based On Deep Learningmentioning

confidence: 99%

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A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

Yang,

Sun,

Zou

et al. 2024

Energies

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“…The need for additional research was noted, including increasing the sensitivity of the color sensor and using multiple sensors to improve contamination detection efficiency. A method using a new CNN design has also been proposed [9]. According to the author's experience, it has shown a high accuracy of 98%.…”

Section: Related Workmentioning

confidence: 99%

Solar Panels Contamination Detection Using CNN

NAJAFLI,

IMAMVERDIYEV

2023

MSJ

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Solar panels, as an important source of renewable energy, are exposed to external factors such as dust accumulation and environmental pollution, which reduces their efficiency. To ensure timely cleaning of panels from contamination, monitoring of their condition is required. Monitoring can be carried out directly by a person or using visual detectors. This paper examines the second case, namely the use of AI to assess panel contamination. The AI used to assess the condition of the panels may be trained on images captured by cameras with a different resolution than the surveillance system in which it is embedded, which may result in the AI's inability to produce results under new conditions. This work evaluates the impact of changing the quality of images processed by AI on its effectiveness. To do this, a CNN (Convolutional Neural Network) model was used and a set of images of clean and dirty solar panels was taken, followed by their separation by resolution. The model was trained on a set of images of one quality and tested on others, and this approach was applied for each resolution. As results were obtained, elements from other sets were added to each set to stabilize the training of the neural network. During the work, the importance of using images of different qualities for training a neural network was assessed, as well as the minimum set to prevent overtraining due to the lack of quality differences. Taking into account the results obtained, it is possible to prevent the creation of inflexible neural networks, as well as save on using the minimum required data to stabilize training.

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“…To address these issues, an AI-based solar panel cleaning robot can be developed, which can perform dry or wet cleaning based on the type of contamination on the solar panel surface. The system incorporates a Convolutional Neural Network (CNN) architecture [1][2][3], which analyses the surface images of the solar panel and identifies the level and type of contamination present. By using this information, the system can determine whether dry or wet cleaning is required and use appropriate cleaning method to clean the solar panel surface.…”

Section: Introductionmentioning

confidence: 99%

AI Based Solar Panel Cleaning Robot

K¹,

SankaraNarayanan²,

Arjun³

et al. 2023

IJETMS

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Among the various renewable energies, Solar energy is one of the most important sources. Solar panel is used to collect the solar energy and convert it into useful electrical energy. The dust accumulated on the solar panel reduce its efficiency to a certain degree. To overcome this problem, efficient techniques to clean the solar panel must be implemented. The proposed model is to clean the dust and bird droppings that has accumulated on the solar panel. An AI-based solar panel cleaning robot is designed which performs dry or wet cleaning based on the dirt or bird droppings, thus reducing water usage. The robot utilizes a Convolutional Neural Network model based on the Visual Geometry Group 16 architecture to detect dirt and bird droppings on solar panels. The robot is designed to perform dry cleaning using a brush and wet cleaning using a water pump, depending on the type of dirt detected. The experimental readings show that the power output of the solar panel increases significantly after cleaning, and the prototype model demonstrates the effectiveness of the robot in detecting and cleaning dirt and bird droppings. The development of such a robot has the potential to improve the efficiency of solar panels and reduce water usage in the cleaning process, making it a more sustainable and eco-friendly solution for maintaining solar panels.

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SolNet: A Convolutional Neural Network for Detecting Dust on Solar Panels

Cited by 28 publications

References 50 publications

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

Solar Panels Contamination Detection Using CNN

AI Based Solar Panel Cleaning Robot

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