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Battery, Fuel Cell, and Super Capacitor are energy storage solutions implemented in electric vehicles, which possess different advantages and disadvantages.
In EVs, the type of energy storage is, together with the drive itself, one of the crucial components of the system.
Another alternative energy storage for vehicles are hydrogen FCs, although, hydrogen has a lower energy density compared to batteries.
This article proposes an integrated approach that combines stationary and vehicle-mounted mobile energy storage to optimize power system safety and stability under the conditions of limiting the total investment in both types of energy storages.
This study proposes a method for detecting and localizing solar panel damage using thermal images. The proposed method employs image processing techniques to detect and localize hotspots on the surface of a solar panel, which can indicate damage or defects.
Yet, several operational and environmental conditions can damage solar panels and lower their performance. To maintain effective operation and maintenance of solar power facilities, prompt diagnosis and localization of solar panel damage are essential. A popular non-destructive testing method for spotting damage to solar panels is thermal imaging.
This person is not on ResearchGate, or hasn't claimed this research yet. This research paper explores the use of deep learning, specifically the YOLOv11 model, in detecting defects in solar panels using thermal imaging. The focus is on two common types of faults: Hotspot Faults and Bypass Diode Faults.
The solar modules got fired at California and North Carolina which are showed as the examples of the faults. The EL images are taken for the healthy panels and the spots of the minor cracks, break images, and finger impregnations for fault-finding. Then, by the PCA and ICA for the image to be processed by the component analysis.