Predictive Reliability Models for Solar PV Using Weather and Module Data

Local weather and module properties profoundly affect the performance and life span of solar photovoltaic (PV) systems. However, most reliability analyses still use static test conditions, which do not allow an effective real-world performance prediction. This PhD will develop predictive reliability models for solar PV systems by using high-resolution weather data and module operation parameters.

The research presented here will start with collecting and pre-processing the datasets, merging local climate data variables (solar irradiance, air temperature, relative humidity, and wind speed) and in-situ measured electric and thermal behaviour of PV modules. As such, statistical and machine learning methods will be used to discover patterns of degradation and failure across different conditions. Particular focus will be on non-linear interactions; for example, simultaneous heat and humidity-induced degradation of module encapsulants or rapid irradiance changes on inverter efficiency.

A second strand of the project will develop and translate these insights into predictive reliability models that can estimate performance degradation, failure probabilities, and remaining useful lifetimes for various PV technologies and sites. These models will be further validated with external data, and, where available, in collaboration with industry partners.

https://www.tudublin.ie/explore/faculties-and-schools/sciences-health/physics-clinical-and-optometric-sciences/people/technical-team/chibuisi-okorieimoh.php

A first-class or upper second-class honours bachelor’s degree in Engineering (Electrical, Electronic, Mechanical, and Renewable Energy Systems) or its equivalent is required; candidates with lower grades must hold a Master’s degree.

Self Funded (Scholarship not available. Fees & Materials to be paid by the student. Materials costs not significant)

If you are interested in submitting an application for this project, please complete an Expression of Interest.

 https://forms.office.com/e/0hCcrv2Gkp