1. Introduction
The use of fossil fuels to produce electric power explicitly increases greenhouse gas emissions (GHGs) in the environment. This climate impact can be considerably reduced by utilising renewable resources, particularly producing electricity from solar energy [
1]. In 2021, the capacity of global renewable production increased by 257 GW and currently amounted to 3064 GW. Solar energy continued to lead the capacity expansion, with an increase of 133 GW (+19%); hence, the global solar capacity reached 849 GW in 2021. For instance, China had the highest contribution of 307 GW of capacity enhancement from its annual capacity of 253 GW in 2020. On the other hand, the USA expanded its solar capacity up to 27% which amounted to 94 GW of solar generation.
Figure 1 shows the enhancement of global solar capacity from 1996 to 2021 of the seven leading countries producing solar energy [
2].
Photovoltaic (PV) cells are one of the promising solar technologies, directly converting sun radiation into electricity at 15% to 20% nominal efficiency. However, the current rate of global solar expansion is impacted by this poor efficiency, and the maximum utilisation depends on multiple environmental factors such as operating temperature, wind velocity, shading loss, hail, snow, air density, sky condition, and dust on the PV surface. Among all these, dust, dirt, and other particles cause soiling losses, which reduce the performance of PV modules [
4]. Dust refers to any particle found in the environment that is less than 10 mm in diameter and generated from many sources such as sand, soil, rocks, contraction debris, volcanic smoke vapor, eroded limestone, and bird droppings [
5]. The dust particles stored in the panels can exacerbate the soiling effect and regularly reduce the overall generation. The deposition of dust particles is influenced mainly by the sun’s angle of inclination and the material of the PV module’s cover in addition to the dust buildup, ambient temperature, tilting angle, soil conditions, and plants in the area. Dust accumulates on the PV surface in three ways: occult deposits (mist, cloud, high humidity, moisture in fog, dew), dry deposits (wind), and wet deposits (rainfall). Depending on the local environmental factors, the dust’s chemical and mineral makeup varies. Due to the fact that the placement of PV installations also impacts dust buildup, the deposit rate is higher close to factories, volcanic regions, and regions vulnerable to sandstorms [
6].
The performance and efficiency of the PV modules differ depending on dust accumulation in the surrounding environment of PV installation. For example, during poor irradiation conditions, dust particles’ size is larger and more significant; hence, the wavelength of the radiation is scattered. The PV panel output falls when the dense coating of dust on the module surface change the optical properties, such as raising light reflection, decreasing transmissivity, and leading to electrical power loss. Moreover, dust causes a temperature variation, resulting in a slight difference in short-circuit current and a decrease of voltage in open circuit, both decreased by 15 to 20% and 2 to 6%, respectively [
7]. In another study, the dust effect of different PV modules showed that 33% of output power was reduced with a dust density of 4.25 mg/cm
in a-Si and CdTe type modules [
8]. It has been also observed that dirty Si solar cells had a reduced efficiency of 66% over a period of six months. On the other hand, 8.41% less power was generated from a dusty module in comparison with a clean one [
9].
Numerous research studies examining the impact of deposited dust on PV panels revealed that tropical climatic conditions, particularly in Asia, are where soiling rates are highest. According to a study on the dust effect conducted in India, between 20% and 25% of the PV-generated energy may be lost in the process. [
10]. According to an experimental study conducted in Lahore, Pakistan, a PV panel can lose between 10% and 40% of its output power due to the rise in surface temperature and dust levels [
11]. Another study in Nepal showed that a solar module’s performance decreased by 29.76% because of natural dust accumulation despite the module being cleaned regularly for five months [
12]. Experiments conducted in China showed that the efficiency of modules ranges from 0% to 26% when the dust density rises to 22 g/m
[
13]. In Southeast Asian, the peak power from PV was reduced by 18% due to dust in a study in Malaysia [
14], whereas the dust accumulation on the PV panel decreased power by 10.8% with a mean relative humidity of approximately 52.24% in an experiment in Indonesia [
15]. According to studies in the Middle East, maximum output power decreased by 34% [
8] in Kuwait, whereas an experiment in Saudi Arabia showed that the output of PV modules might decrease by 26% to 40% [
16]. A study conducted in Iran showed that a lack of rain for 70 days causes 6.0986 (g/m
) of dust to accumulate on the surface and reduce output power by 21.47%, which is a 289-kWh energy reduction in provided energy for each 78 4.845 kW PV system [
17]. Additionally, six different places in northern Oman were used to gather natural dust for a study, and its characteristics were studied. It revealed that if a PV module is not cleaned for three months, soiling loss causes a 35–40% reduction in power output [
18]. However, it is clearly observed that the factors are varied at different locations according to the types of dust, and the impact on PV cells is inevitable. Therefore, it is highly important to clean the panels at regular intervals to maximise PV generation. To ensure clean panels, the detection of dust is a prime need.
To detect the amount of dust on the panels, multi-dimensional approaches such as thermal imaging, image processing, sensors, cameras with IoT, machine learning, and, deep learning are used. Out of these methods, in the thermal imaging detection method a thermal image scanner or an infrared camera detects or captures the infrared energy of objects since infrared light and heat are not visible to human eyes. A thermal camera takes infrared pictures showing heat emitted from an object or material at temperatures above zero degrees and sends a message to the appropriate command center [
19]. Such a technique was used by Phoolwani et al. [
20] to detect the change in performance of PV panels in both favorable and unfavorable circumstances. The researchers also observed
through the PV analyser, and the thermal image was used to pinpoint the PV locations that needed to be cleaned. Cubukcu et al. [
21] worked with 19 separate PVPSs in Turkey to detect defects using thermal imaging which performed better in terms of effectiveness and efficiency than other available methods.
IN contrast, IoT and sensor-based techniques are used for both detection and periodic cleaning of the panels. A prototype was proposed by Thomas et al. [
22] where a wet cleaning process was introduced, and this method saved the surface from being scratched and resulted in an energy-efficient cleaning system. In another study, Zainuddin et al. [
23] introduced a live monitoring method for solar cells with the help of IoT, and a smartphone app was used to monitor the electricity produced and clean the PV surface as needed. Related research was conducted by Mohammed et al. [
24] that employed Arduino with a dust sensor (DSM501A) which is low-cost and smaller than other systems for both detection and automatic cleaning system. Experimental performance analysis showed that the robot involvement in the solar panel improved the system’s overall efficiency in the work of Kumar et al. [
25]. Recently, satellite remote sensing has been widely used in various sectors, such as solar panel dust or sand detection, geolocation, soil quality monitoring, rice paddy status, etc. as shown by Minh et al. [
26]. Such an approach is used by Google Earth Engine (GEE) with the Dry Bare Soil Index (DBSI) method that showed a detection accuracy of 89.6% as found by Supe et al. [
27].
Digital image processing is another prevalent method of detecting dust. In terms of image processing, there is no physical connection between the solar panel and the camera. For instance, Abuqaaud et al. [
28] presented a novel approach for dust detection using image processing with computer vision. In this method, hue layer was used to extract features from HSV colour with a co-occurrence gray level matrix, and finally sort clean and dirty panels using a linear method of classification. For further development in the detection of dust, two methods, namely digital and infrared cameras, were used by Tribak et al. [
29]. By using techniques such as linear regression, spectral decomposition of light, and other image processing techniques, dust can be detected on the exterior of PV modules with an accuracy of 90%. The researchers also proposed another image processing method where they formed a correspondence curve among the telltale image entropy and the generated power. The experiment was conducted with different concentrations of dust, where it was found that power production was nearly zero with the panels 100% covered with dust. Aside from the advantages of available techniques for dust detection using thermal imaging and IoT, it has some drawbacks. For instance, the IoT system must be connected to a sensor whose efficiency can depreciate with time. Furthermore, thermal imaging needs a very high-quality camera and sophisticated software to produce correct results. Both processes are high maintenance and costly. Moreover, image processing systems have a low accuracy rate of detection. Furthermore, the dust types are not identical in all the places on Earth. The systems need to be tangible, which is difficult for IoT and thermal imaging detection systems.
The recent development of artificial intelligence (AI)-based dust detection methods has introduced a new perspective and is becoming more popular [
30]. Different approaches, such as the measurement of the dimensions of dust particles using computer vision for high-resolution images by Igathinathane et al. [
31], identification of particles using random forest by Maitre et al. [
32], and k-nearest neighbors (
k-NN) by Proietti et al. [
33], have been used by researchers for the classification and detection of dusty panels. Deep learning models were also used for such classification. With the information gathered from pictures of dust on solar panels, Saquib et al. [
34] created an artificial neural network (ANN), and the output was preserved in the form of voltage and current. Other parameters, such as irradiance, current, and voltage, were measured with the help of LDR and multi-meter. The developed neural network had only one hidden layer with nine neurons. Thus, the output voltage of the panels was forecast with the irradiance and amount of dust as the input. For the same purpose, a convolutional neural network (CNN) method was proposed by Mehta et al. [
35]. Using web-supervised learning, the impact area of dust obtained from the predicted localisation masks is classified into soiling types. Bi-directional input-aware fusion (BiDIAF) was used for featuring the data. The accuracy of power loss prediction promised, which is about 3% and localisation about 4%, improved by 226 BiDIAF. In a weakly supervised manner, localisation improved by about 24% in that research. For the prediction of the concentration of uneven dust gathering, a deep residual neural network in conjunction with image processing and cleaning methods was used by Fan et al. [
36], where they achieved R2 and mean absolute error (MAE) of 78.7% and 3.67%. In another study, 30,000 images were taken with binary labeling, and the power loss was calculated keeping the same irradiance level. The CNN LeNet model was employed with custom layers of dropouts and pooling, where Maity et al. [
37] achieved an accuracy of 80% with the mean squared error of 0.0122. Deep CNN architectures were been applied by Zyout et al. [
38] to build a model using a dataset of 599 images. The dataset was applied to AlexNet, LeNet, and VGG-16 models where AlexNet achieved an accuracy of 93.3%. A study was conducted on PV degradation and irregularity patterns using reviewed different machine and deep learning methods using computation period, characterization techniques, dataset, and feature extraction mechanisms [
39]. In another study, a deep belief network was developed to detect the dust on PV panels, and the proposed model achieved higher accuracy in comparison with other machine-learning-based models [
40]. Another study considered the integration of Mobile-Net and VGG-16 CNN techniques for the evaluation of solar panels, combined with the physical lotus effect methodology that allows for solar panel maintenance [
41]. Similar to this, for a PV performance study, the effectiveness of various machine learning algorithms, including auto-encoder long short-term memory (AE-LSTM), Facebook-prophet, and isolation forest, was evaluated. The results offer clear insights to assist in making an informed decision [
42]. In further study, researchers kept six PV modules in Sohar city, Oman, where a deep-learning-based modular neural network was used to investigate the impacts of dust and temperature on PV power production [
43].
However, the world has envisioned notable changes with multiple applications of neural networks where the models can learn to predict by themselves. Such models can be trained with any local dataset according to the region where the panels are set up. Thus, a new possibility for the improvement of dust detection through research emerges. However, for building such models, a vast dataset is needed. Unfortunately, such datasets are not publicly available for solar panel dust analysis to the best of the author’s knowledge. The scarcity of good and balanced datasets makes it difficult to make a model with appropriate parameters, and a distinct research gap for assessing state-of-the-art algorithms with appropriate datasets exists. In this paper, a new dataset of dusty and clean panels is introduced which is free from class imbalance. Applying the current state-of-the-art (SOTA) algorithms, this new dataset has accuracy of nearly 100% on test sets. Then, a new CNN architecture named SolNet, which deals specifically with dust detection on PV panels, is proposed. Thereafter, the performance of the proposed SolNet and other SOTA is compared to validate its efficiency, and outcomes are discussed. Finally, both dataset and SolNet are proposed for benchmarking for future research.
The following provides a summary of the contributions in this article:
A new dataset of the dusty and clean solar panel is introduced that is free from class imbalance.
The current stateoftheart (SOTA) algorithms are performed nearly 100% accurately on test sets of our dataset.
SolNet, a CNN architecture that deals specifically with dust detection on solar panels is proposed and tested.
The proposed model is evaluated and compared with SOTA to validate its efficiency.
Both datasets and SolNet are proposed as benchmarks for future research endeavors.
This article has been arranged into several sections. In
Section 2, the CNN-based dust detection methods are discussed. The following
Section 3 illustrates the experimental setup with the necessary specifications. Results analysis and discussion are discussed in
Section 4 and
Section 5. Finally,
Section 6 contains the concluding remarks and future works about the research.