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.
School
Photovoltaic and Renewable Energy Engineering
Research Area
Photovoltaics, Solar Modules, Luminescence Imaging, Machine Learning
- Research environment
- Expected outcomes
- Supervisory team
- Reference material/links
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.
Applicant suitability: familiarity (basic skills, experience desirable) with coding in python (data processing, analysis, visualisation); basic understanding of machine learning systems (statistics, optimisation).
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.
Deliverables: prepared training datasets (cell luminescence images); performance validation (statistical assessment) and visual examples of model output (generated solar cell luminescence images) for each configuration of learning model; analysis of the influence of varying training dataset, model architecture, and learning methodology.
