Machine Learning Based Identification and Classification of Field-Operation Caused Solar Panel Failures Observed in Electroluminescence Images
Stefan Bordihn, Andreas Fladung, Jan Schlipf, Marc Köntges
Abstract
Failure or degradation effects lead to power losses in solar panels during their field operation and are identified commonly by electroluminescence (EL) imaging. Some failures like potential-induced degradation (PID) and light and enhanced temperature induced degradation (LeTID) require an identification of the EL pattern over the entire solar panel. As the manual process of analyzing patterns is prone to error, we seek for an automatic detection of these failure types. We predict automatically the failure types PID and LeTID by adopting the principal component analysis (PCA) method in combination with a k-nearest neighbor (kNN) classifier. We increase the explained variance of the PCA by 4 %abs using a Gaussian blurring preprocessing step and gain insights into the basic mechanism of the machine learning algorithm by analyzing schematic EL images. The kNN classifier predicts the failure classes in the same way as the expert. Finally, we work with a larger test dataset of 40 similar images to mimic a field-typical user case and meet again the expert classification.