Machine Learning based Identification and Classification of Field-Operation caused Solar Panel Failures observed in Electroluminescence Images
preprintposted on 2021-09-23, 06:58 authored by Stefan BordihnStefan Bordihn
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.
Email Address of Submitting Authorbordihn@isfh.de
ORCID of Submitting Authorhttps://orcid.org/0000-0001-7077-7905
Submitting Author's InstitutionInstitute for Solar Energy Research, Emmerthal, Germany
Submitting Author's Country