One critical maintenance challenge in photovoltaic installations is detecting hot spots, localized overheating defects in solar cells that drastically reduce efficiency and can lead to...
In practice, it is observed that the YOLO algorithm is many times faster than the Faster R-CNN in high-density solar panels. Therefore, the applied method is the safest choice for automatic hotspot
Thermal imaging inspection of PV modules is an indispensable aspect of PV plant operation and maintenance. This article introduces a lightweight detection algorithm for hot spots in
This model is a detection method for hot spots of PV panels based on the latest generation of the one-stage object detection YOLOv5 network, which is improved to achieve rapid
With recent significant achievements and growth of machine learning methods in image processing and technical diagnosis, it is greatly possible to develop a machine learning model to
The formation of hot spots in solar panels is a critical aspect of a photovoltaic system. These anomalies can significantly affect the efficiency and longevity.
This project presents an IoT platform working on artificial intelligence (AI) which automatically detects hot spots in PV modules by analyzing the temperature differentials between
For this purpose, two AI (Deep learning and machine learning) were trained and tested in a real PV installation where hot spots were induced. The system was able to detect hot spots with a sensitivity
This research successfully demonstrates an innovative approach to the autonomous detection and classification of defects in photovoltaic panels using convolutional neural networks
Hot spots are common defects in photovoltaic (PV) modules that can lead to performance degradation and even pose a fire hazard. This study proposes an online detection methodology for
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