Image segmentation using K-means color quantization and density-based spatial clustering of applications with noise (DBSCAN) for hotspot detection in photovoltaic modules

Abstract

The increasing demand for the use of solar energy as an alternative source of energy to generate electricity has multiplied the need for more photovoltaic (PV) arrays. With the growth of the PV manufacturing industry, automation for defect detection is seen as a great potential in ensuring the quality of these PV modules. Hotspot formation due to defects is detrimental to the performance of PV devices. Thus this research aims to detect and isolate hotspot areas in PV modules by applying two machine learning techniques, namely K-means color quantization for pre-processing, and density-based spatial clustering of applications with noise (DBSCAN) for processing, in the images captured by an infrared camera. In the preprocessing, K-means clustering algorithm produced a quantized color image represented by the contours while in the processing or clustering part, DBSCAN resulted in the segmentation of the image, isolating the hotspot. Further investigation of the PV module through visual inspection found a crack in one of the solar cell where the hotspot occurred.