Dust detection in solar panel using image processing techniques: A review

Dantas, Gabriel Moura; Mendes, Odilon Linhares Carvalho; Maia, Saulo Macêdo; Alexandria, Auzuir Ripardo de

doi:10.33448/rsd-v9i8.5107

Cited by 20 publications

(8 citation statements)

References 32 publications

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“…The use of image processing methods has been proposed to detect dust on solar panels in order to optimize their cleaning processes [6]. The review reveals that despite the low cost and high efficiency of the proposed methods, the number of relevant studies is limited.…”

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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Section: Related Workmentioning

confidence: 99%

Solar Panels Contamination Detection Using CNN

NAJAFLI,

IMAMVERDIYEV

2023

MSJ

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“…However, using flying robots and drones with high-resolution cameras has proved to be a more effective technique, they can capture images in more detail [21] . There also exist errors from the image processing method, which requires advanced methods to transform the images [22] .…”

Section: Introductionmentioning

confidence: 99%

Solar panel surface dirt detection and removal based on arduino color recognition

Olorunfemi¹,

Nwulu²,

Ogbolumani³

2023

MethodsX

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“…Another methodology of detecting dust was introduced in [17], where the PV output voltage and current are monitored to operate the washing unit when the output power is less than 50% of the rated power during the daytime. Researchers in [18,19] investigated the feasibility of imaging process technology to detect dust on PV panels. An approach based on optical imaging and the routine measurement of aerosols was also explored in [20] to obtain PV panels' dust.…”

Section: Introductionmentioning

confidence: 99%

“…All results were normalized to be a percentage of each individual comparative aspect. It should be noted that the comparison in Figure 1 is not applicable for any detecting and cleaning approach; however, it is valid for the strategies in the literature, i.e., [9][10][11] and [16][17][18][19], as they compared detection systems with no additional imaging or sensing devices. Therefore, this present study proposes an intelligent computational system to detect the dust level on PV panel surfaces without integrating any external imaging, measuring, or monitoring devices.…”

Section: Introductionmentioning

confidence: 99%

A Sensorless Intelligent System to Detect Dust on PV Panels for Optimized Cleaning Units

Alfaris

2023

Energies

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Deployment of photovoltaic (PV) systems has recently been encouraged for large-scale and small-scale businesses in order to meet the global green energy targets. However, one of the most significant hurdles that limits the spread of PV applications is the dust accumulated on the PV panels’ surfaces, especially in desert regions. Numerous studies sought the use of cameras, sensors, power datasets, and other detection elements to detect the dust on PV panels; however, these methods pose more maintenance, accuracy, and economic challenges. Therefore, this paper proposes an intelligent system to detect the dust level on the PV panels to optimally operate the attached dust cleaning units (DCUs). Unlike previous strategies, this study utilizes the expanded knowledge and collected data for solar irradiation and PV-generated power, along with the forecasted ambient temperature. An expert artificial intelligence (AI) computational system, adopted with the MATLAB platform, is utilized for a high level of data prediction and processing. The AI was used in this study in order to estimate the unprovided information, emulate the provided measurements, and accommodate more input/output data. The feasibility of the proposed system is investigated using actual field data during all possible weather conditions.

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Dust detection in solar panel using image processing techniques: A review

Cited by 20 publications

References 32 publications

Solar Panels Contamination Detection Using CNN

Solar Panels Contamination Detection Using CNN

Solar panel surface dirt detection and removal based on arduino color recognition

A Sensorless Intelligent System to Detect Dust on PV Panels for Optimized Cleaning Units

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