Lower Solar PV Cost by a Combination of Luminescence Images and Machine-Learning
Summary
This project will develop methods to sort cells using machine learning and luminescence images. The training will be based on more than 1 million solar cell luminescence images and associated electrical parameters. The UNSW research team will train the algorithm to predict the degradation extent when the initial images are provided as the input.Using innovative combinations of luminescence-based imaging and machine learning, this project aims to develop novel in-line characterisation tools for cells and modules. In doing so, this will reduce the cost of PV systems by delivering two key technological outcomes: Replacing the standard current-voltage sorting process with faster and cheaper luminescence imaging. This will significantly improve the durability and reliability of PV systems.The partnership with BT Imaging will allow the developed methods to be integrated in characterisation tools that are manufactured in Australia.