Early hotspot detection in photovoltaic modules using color image descriptors: An infrared thermography study

This paper proposes a new framework for early hotspot detection in the photovoltaic (PV) panels using color image descriptors and a machine learning algorithm. In the proposed approach, the acquired thermographic images of PV panels are divided into non-overlapping regions, and then color image descriptors are computed for the regions. The color descriptors are then used as features to train different machine learning algorithms to classify the PV panels into three classes (ie, normal, hotspot, and defective). After extensive testing and comprehensive analysis, the experimental results show that Red-Green Scale-Invariant Feature Transform (rgSIFT) descriptor with k-Nearest Neighbor (k-NN) outperforms all other images descriptors and machine learning combinations with an accuracy rate of 98.7%. The experimental results also show the effects of the size of non-overlapping regions on the classification accuracy. It is observed that the classification accuracy decreases as size is increased or decreased around the optimal non-overlapping region image size of 71 x 71 pixels. The proposed method has a significant role in carbon-free cities and can easily be implemented to inspect the PV system.

Automated summary

This study introduces a new method for early detection of hotspots in photovoltaic panels using color image descriptors and machine learning algorithms. Experimental results show that the combination of rgSIFT descriptor and k-NN algorithm outperforms other methods with an accuracy rate of 98.7% under the optimal non-overlapping region size.