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To improve the accuracy of fault detection in photovoltaic farms, Roberto et al. 13 proposed to use improved Mask R-CNN to detect the photovoltaic fault and published a newly annotated dataset. Chen et al. 14 proposed a multi-scale Faster RCNN model for detecting defects in Electroluminescence images of photovoltaic cells.
Detection of cracks in solar photovoltaic (PV) modules is crucial for optimal performance and long-term reliability. The development of convolutional neural networks (CNNs) has significantly improved crack …
In solar panel inspection, understanding why a model classified a particular cell as cracked is vital. Incorporating interpretability and explain ability mechanisms into CNN …
The objective of this mini review is to present and summarize the recent studies on the effect of PV shading on crop cultivation (open field system and greenhouses integrated …
Solar photovoltaic (PV) systems are becoming increasingly popular because they offer a sustainable and cost-effective solution for generating electricity. PV panels are the …
Since rooftop rainwater harvesting can be incorporated with a solar energy capture system to alleviate both the water and energy crisis, there exists a need for the evaluation of water …
Photovoltaic (PV) panels are widely adopted and set up on residential rooftops and photovoltaic power plants. However, long-term exposure to ultraviolet rays, high temperature and humid environments accelerates the …
The maintenance of large-scale photovoltaic (PV) power plants is considered as an outstanding challenge for years. This paper presented a deep learning-based defect …
The various deep features of the images that are obtained aerially are extracted by CNN. When large scale PV farms are taken into consideration, the proposed deep learning …
Learning rate of 0.01, RMSProp optimizer, Categorical Cross Entropy as loss function, and batch size of 32 is used for training. 3.5. Hotspot Identifier To identify the region …
A solar panel comprises photovoltaic (PV) cells that convert sunlight into DC electricity. In a home setup, the DC current is then sent to an inverter, which is converted into AC, the type of ...
Detecting and replacing defective photovoltaic modules is essential as they directly impact power generation efficiency. Many current deep learning-based methods for detecting defects in ...
Therefore, the PV cells in a solar panel are components of the PV module, as the module is composed of individual cells. CNN detects and analyses PV cells within the PV …
In recent years, CNN has emerged as a powerful tool in crack detection, enhancing the accuracy and efficiency of PV module inspection [ 6 ]. These deep learning algorithms have demonstrated their effectiveness in detecting and classifying cracks in solar PV modules, enabling timely and effective maintenance and repair.
In Greco et al. (2020), a CNN-based YOLOv3 architecture is proposed for defect detection in PV panels. In Akram et al. (2020) deep learning framework based on transfer …
To improve the efficiency of solar panels, the removal of surface contaminants is necessary. Dust accumulation on PV panels can significantly reduce the efficiency and power …
As the defects present on the PV panels can lead to the change in both the power generation efficiency and the heat flow of the panel, the available PV panel condition …
This difficulty arises because the CNN model is trained on a specific dataset that has a limited scope and does not account for variety in design, texture, and environment. Therefore, the CNN model is not able to recognize patterns and features that are unique to specific solar panels.
Although it has a faster detection speed, detection accuracy is lower than the methods that are based on normal convolution neural networks. Li et al. 12 proposed a deep convolution neural network for detecting photovoltaic module defects by using the aerial infrared images obtained from unmanned aerial vehicles.
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