Advance Degradation Modelling of Photovoltaic Modules and Materials! (Advance)

Different modeling approaches (statistical, chemical-physical-electrical) are to be developed and applied in order to identify correlations between the performance degradation of PV modules in operation, the specific degradation behavior of the materials and material composites used, and the stress conditions acting on them, and to use them for innovative material developments and predictive maintenance specifications.

This includes:

  • Automated data processing: feature selection, image analysis (neural networks – machine learning), data reduction (conversion of experimentally obtained digital information into a corrected, ordered and simplified form).vv
  • Statistical modeling of correlations of data/measurements of a comprehensive existing database (multiple, time-resolved characterization data of PV modules during various accelerated aging tests)
  • Creation of a predictive model (chemical/physical/electrical) for the long-term durability and reliability of PV materials and modules
  • Validated degradation models for PV materials/modules for early detection of aging, creation of optimized accelerated aging tests (design of experiment) and predictive maintenance specifications

Facts
Data-Driven, Smart & Secure Systems
Department Electronic Engineering
FFG
from January 2021 to December 2022
DI Karl Knöbl, MSc
DI Karl Knöbl, MSc

Senior Lecturer/Researcher

+43 1 333 40 77-2048karl.knoebl@technikum-wien.at