Supervisor:
Huai Wang

Co-Supervisor:
Ariya Sangwongwanich

Assessment Committee:
Farshid Naseri (Chair)
Giovanni Spagnuolo, University of Salerno, Italy
Ke Li, University of Nottingham, UK

Moderator:
Subham Sahoo

Abstract:

Photovoltaic (PV) systems are gradually becoming a vital constituent of future power systems, where long-term availability and safe operation are significantly dependent on the reliability of critical components such as PV arrays, DC-link capacitors, and power semiconductor devices. However, subjected to prolonged operation and complex electro-thermal mechanical stresses, these components are prone to failure resulting in unscheduled downtime, substantial economic loss, and even catastrophic incidents. Thus, it is urgent to develop tailored fault diagnosis, condition monitoring, and remaining useful life prediction methods for the above-mentioned critical components, which can effectively prevent catastrophic failures, reduce unscheduled downtime, enable predictive maintenance to avoid over-maintenance and reactive maintenance, and significantly reduce maintenance costs. Moreover, it is worth noting that advanced technologies such as sensors, data acquisition systems and artificial intelligence provide new opportunities to facilitate the proactive predictive maintenance paradigms. Thus, this PhD project is dedicated to developing advanced fault diagnosis, condition monitoring, and remaining useful life prediction methods to ensure the safe and reliable operation of critical components in PV systems. It aims to enhance system safety, stability, and economic efficiency while further promoting the digital transformation of power electronic systems.