Student: Alois Klink

Photovoltaic (PV) solar energy production is the fastest growing form of renewable energy, with forecasts of generation doubling by 2024. This growth is led by a decrease in capital cost, causing the relative cost of PV maintenance to increase, from ~50% of the total cost of UK’s large-scale PV farms in 2019, to ~67.5% in 2030. Because of this, research is being undertaken into optimising solar PV maintenance using automated fault detection using AI. In this work, infrared thermography is used to capture an image of the temperature of a solar PV module. State-of-the-art deep-learning-based image-classification algorithms are then used to detect if there is a fault and the type of the fault providing warning to farm operator.