This paper is organised as follows: section II outlines the proposed review methodology, section III explains the significance of studying dust accumulation and its impact on PV panels performance, section IV discussed the impact of
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
Siyuan Fan et al. developed a new method based on a dust concentration and photoelectric conversion efficiency (DC-PCE) model that can be used under radiation conditions up to 1000 W/m 2. This model examines
The particle deposition on the surface of solar photovoltaic panels deteriorates its performance as it obstructs the solar radiation reaching the solar cells. In addition to that, it may cause
To explore the influence of different factors on particle deposition, four crucial factors, including particle size, wind speed, inclination angle, and wind direction angle (WDA),
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
In addition, the structural design of PV panels can affect the accumulation of dust and the potential degradation in performance, it was found that frameless PV panels experience uniform distribution of dust, while the distribution of dust in
Figure 6 a shows that out of the chosen images, 220 were classified as dust PV panels and 82 were classified as without dust PV panels. Figure 6 b represents the results in percentage form, with 72.8% of the images classified as dust PV panels and 27.2% classified as without dust PV panels.
The integrated methodology successfully detected and localized dust particles on PV panels. The findings of this research have significant practical implications for the solar energy industry. The integrated approach offers an efficient and automated solution for monitoring dust accumulation on PV panels.
To identify dust particles on photovoltaic panel, image processing technique is used. Image processing involves several steps. These steps are image acquisition, pre-processing, segmentation, feature extraction, classification, post-processing, visualization and reporting. Block diagram of these process is presented in Fig. 1.
Characteristics of dust particles and depositions have a significant impact on the performance of PV panels. In this regard, Kazem et al. have provided a comprehensive review of the dust characteristics of six dust pollutants and cleaning methodologies impact on the technical and economic aspects of cleaning (Kalogirou 2013 ).
The impact of dust accumulation on the thermal performance of photovoltaic (PV) systems primarily manifests in the alteration of PV module temperature.
Using a deep learning architecture, the images were classified into two categories: PV panels with dust and PV panels without dust. The results were presented in the form of a confusion matrix. Figure 6 a shows that out of the chosen images, 220 were classified as dust PV panels and 82 were classified as without dust PV panels.
