Automatic Fault Detection in Utility-scale Photovoltaic (PV) Systems with Machine Learning

Are you eager to make a meaningful contribution to the field of renewable energy while gaining invaluable data analytics skills for your future career? We invite you to embark on a transformative journey with us as we delve into the fascinating realm of solar power systems powered by Machine Learning.

Automatic detection of faults in solar power systems allows for maximised energy production, reduced downtime and maintenance costs, and grid stability. Our project focuses on advancing the capabilities of fault detection and diagnostics in utility-scale PV systems through the utilisation of multivariate time series analysis and state-of-the-art Deep Learning approaches such as generative AI models. This research aims to enhance the monitoring and maintenance of such systems by developing an automated approach to identifying potential faults or anomalies using a combination of various time series signals.

In this project, you will collaborate with world-class Computer Scientists, and gain hands-on data analytics skills on real-world industrial datasets. Candidates with Computer Science, Electrical Engineering, or Statistics background, who possess a burning curiosity and wish to push boundaries, are highly desirable. Don't miss out on this incredible opportunity to pioneer advancements in renewable energy research while acquiring skills that will define your future success.