EU PVSEC Programme Online
EU PVSEC 2021, 6 - 10 September 2021
Presentation: 5CV.2.11 Deep Learning Based Image Feature Extraction for Predicting Climate Related Degradation of PV Modules
Type: Visual
Date: Wednesday, 8th September 2021
13:30 - 15:00
Author(s): L. Neumaier, J. Scherer, C. Schwarzlmüller, B. Kubicek, F. Mödritscher, C. Hirschl
Presenter / Speaker: L. Neumaier, SAL Silicon Austria Labs, Villach, Austria
Event: Conference Conference
Session: 5CV.2 Operation, Performance and Maintenance of PV Systems
Topic: 5. 3 Operation, Performance and Maintenance of PV Systems
Keywords: Machine Learning, Deep Learning, Image Processing, Accelerated Aging, Lifetime Prediction, Electroluminescence Imaging
Summary / Abstract: To avoid long time failures of PV power plants, reliability testing and modeling of novel photovoltaic (PV) material combinations has gained importance within the last years. In this work, conducted within the research project ADVANCE!, we present an image based deep learning approach to extract degradation-relevant information from aged PV modules. The basis of the work is a comprehensive database, established within the project INFINITY, which includes extensive time-resolved measurement and characterization data of aged PV modules, that have been subjected to precisely defined accelerated aging scenarios. Within this work, electroluminescence images serve as input for the development and application of a deep learning approach to extract degradation parameters. For defect classification and localization, we use a Mask R-CNN architecture, which allows instance-level segmentation and classification in parallel. The extracted information can be used for subsequent statistical analysis, where additional information is available, to make predictions about the operational lifetime of a module under different climatic conditions. Although the amount of data is comparatively small and the distribution of the defect classes within the dataset is not uniform, the available results show the potential of our approach.