The cumulative global photovoltaic (PV) capacity has been growing exponentially worldwide, primarily due to the installation of grid-connected photovoltaic (GCPV) plants. Therefore, fault detection and diagnosis are essential for the efficiency, reliability, and safety of solar photovoltaic (PV) systems. This project will investigate different AI-based models to learn, detect, and classify various faults and mismatching conditions linked with PV systems. The proposed models will be practically deployed and experimented on our new state-of-the-art photovoltaic system at York University. Not only physical PV faults will be considered, but we will also study the impact of micro-cracks and hotspots on the degradation mechanism of the PV system.
For more information about the PhD project, please contact Dr Mahmoud Dhimish: [Email Address Removed]
Candidates must have (or expect to obtain) a minimum of a UK upper second-class honours degree (2.1) in an Engineering discipline (Electronics, Electrical, or Energy), Physics, Computer Science or in a related subject.
Candidates with prior knowledge or experience in handling any techniques such as photovoltaics (PV) characterisation, machine learning, artificial intelligence, power electronics, or MATLAB will be desirable, although it is not an essential requirement.
How to apply:
Applicants should apply via the University’s online application system at https://www.york.ac.uk/study/postgraduate-research/apply/. Please read the application guidance first so that you understand the various steps in the application process.