Lower Solar PV Cost by a Combination of Luminescence Images and Machine-Learning

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This project aims to develop machine learning algorithms to manufacture solar cells cheaper and with less manufacturer faults. Reducing the cost of solar photovoltaic (PV) energy is important to realise its potential as a major energy source. It is estimated that around 20% of modules installed in Australia will under-perform, making financing PV systems risky and ultimately hurting Australian consumers. This project will develop ways to identify faulty modules earlier, to reduce risk and lower the cost of PV power-plants. 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. Labelled datasets will be created and luminescence images of various module types will be acquired. The modules will then be exposed to fast degradation processes to accelerate various types of degradation. Luminescence images will be taken during the degradation process. This will create a database of images, labelled with the extent of the degradation. The UNSW research team will train the algorithm to predict the degradation extent when the initial images are provided as the input.

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