PROJECT PROFILE: Underwriters Laboratories (FY2018 Photovoltaics)

Project Name: A Data-driven Approach to Real-world Degradation of Backsheets
Funding Opportunity: Solar Energy Technologies Office Fiscal Year 2018 Funding Program (SETO FY2018)
SETO Research Area: Photovoltaics
Location: Northbrook, IL
SETO Award Amount: $1,500,000
Awardee Cost Share: $375,000
Principal Investigator: Kenneth Boyce

-- Award and cost share amounts are subject to change pending negotiations --

This project performs data modeling of photovoltaic (PV) modules in various environments to determine the effect of different environmental factors on performance. Testing solar panels in artificial environments that mimic the conditions of typical systems allows researchers to predict performance, identify failures, and propose solutions. The in-depth analysis, feedback loops, and statistical results of this work will provide information about performance-property relationships and lend insight into system reliability and operational lifetime. The results of this project will help PV developers address stability and operational limitations and incorporate these findings into new design architectures for enhanced PV performance.

APPROACH

Researchers are conducting field surveys of various PV sites and evaluating PV module performance according to location and climate. Characterization techniques will be used to examine the surface properties and chemical changes in PV backsheets, the protective covering on PV modules, when exposed to different environmental effects. Module responses to pollution, humidity, temperature and abrasion will be used in mathematical models to inform new backsheet material choices to improve resilience to degradation.

INNOVATION

Evaluation of PV modules subjected to real-world conditions during operation can help researchers isolate failures and predict performance limitations to better identify areas of improvement. The results of this work could be adopted by the PV research community and used as training data for future modeling studies.