loading page

Machine Learning based Identification and Classification of Field-Operation caused Solar Panel Failures observed in Electroluminescence Images
  • Stefan Bordihn
Stefan Bordihn
Institute for Solar Energy Research

Corresponding Author:[email protected]

Author Profile

Abstract

Failure or degradation effects lead to power losses in solar panels during their field operation and are identified commonly by electroluminescence imaging. Failures like potential induced degradation and light and enhanced temperature induced degradation require an identification of the electroluminescence pattern over the entire solar panel. As the manual process of analysing patterns is prone to error, we seek for an automatic detection of these failure types. We predict automatically the failure types potential induced degradation and light and enhanced temperature induced degradation by adopting the principle component analysis method in combination with a k-nearest neighbour classifier.
May 2022Published in IEEE Journal of Photovoltaics volume 12 issue 3 on pages 827-832. 10.1109/JPHOTOV.2022.3150725