Use machine learning to identify defects in solar cell luminescence images

Description of field of research:

Accurately identifying defects in solar modules from luminescence images is currently a manual process and requires experienced domain experts. This approach is time-consuming, prone to error, and does not sufficiently scale to that required by the growing photovoltaic energy industry.

This project will use machine learning models (generative representation learning) to identify and classify visible defects in luminescence images of photovoltaic modules. this will enable a robust, scalable, and automated methodology to monitor and accurately identify defective modules, such that they can be replaced, and either re-used, repaired or recycled.

Through this research project you will gain an understanding of common defects in solar cells and modules, and how these present themselves in luminescence images, as well as develop valuable skills regarding image data analysis and the development and training of machine learning models